About Codat
Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.
Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.
We are looking for a deeply experienced commercial banking practitioner to join Codat as we build the Advisory Intelligence layer for commercial banking. You will be the voice of the banker inside our product organization — the person who knows, from years of first-hand experience, how relationship managers and treasury officers actually serve their clients, where the revenue lives in a commercial banking book, and what an insight has to look like before a banker will act on it. This is a product subject matter expert role first and foremost: you will shape the data and insights modules that Codat's Banking Context Engine generates for banker users. You will use these expertise in front of prospects and customers, working alongside our GTM team in conversations with some of the largest commercial banks in North America and the UK.
The Role
This role sits at a unique intersection between Product, GTM and strategy covering the following broad intersection.
Design actionable insights: Work hand in hand with Product Managers to deliver the substance of the insight modules our Banking Context Engine delivers — defining what signals matter across Treasury & Payments, FX, supply chain finance, working capital and lending, what a banker needs to see to act, and what \"actionable\" genuinely means in the context of a client conversation. You'll separate insights that generate fee revenue and deepen relationships from noise that erodes banker trust.
Be the voice of the banker: Bring the daily reality of commercial bankers and treasury management officers into every product decision — coverage models, portfolio reviews, pre-call preparation, annual pricing conversations, cross-sell motions — so that what we build fits how banks actually work rather than how outsiders imagine they work.
Consult into the product build: Actively collaborate with our product and engineering teams as they build the Banking Context Engine and its MCP integration with Microsoft Copilot, translating banking domain knowledge into concrete requirements, evaluation criteria, and quality bars for generated insights.
Support GTM as the banking SME: Join sales conversations, demos, and executive meetings with prospect and customer banks as the credible domain expert in the room. Support discovery by speaking the language of bank product heads, treasury management leaders and commercial banking executives, and help our GTM team pressure-test messaging against how banks really buy.
Sharpen how banks measure value: Help build the ROI narratives that connect Codat's insights to outcomes banks care about — fee revenue growth from the existing book, expanded coverage without added headcount, and client retention against fintech encroachment.
Build institutional knowledge: Codify what you know — banker workflows, product economics, org structures, buying dynamics — into reusable assets that raise the banking fluency of the whole company.
What you'll bring to the team
Substantial first-hand experience inside commercial banking — as a treasury management officer or sales/product lead, commercial relationship manager, or commercial product manager serving mid-market and SMB clients
A working command of how commercial banks make money from operating relationships: treasury and payments fee structures, deposit economics, FX, card, supply chain finance and working capital products, and how these are priced, bundled and cross-sold
Deep familiarity with how banks organize coverage — RM and TMO territory models, portfolio segmentation, call planning, and the practical constraints that determine whether a client gets proactive advice or none at all
The ability to translate domain expertise into product requirements: you can tell a product team precisely why an insight will or won't land with a banker, and articulate what would change that
Credibility and composure in front of senior bank executives — you have sat across from (or been) the heads of treasury management, commercial banking or product at a significant institution, and can hold that room
Genuine curiosity about AI-assisted banking workflows — familiarity with copilot-style tools (e.g. Microsoft Copilot) and an informed point of view on where AI genuinely helps bankers.
Comfort operating in a fast-moving scale-up with significant opportunity to influence our future success
About Codat
Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.
Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.
We are looking for a deeply experienced commercial banking practitioner to join Codat as we build the Advisory Intelligence layer for commercial banking. You will be the voice of the banker inside our product organization — the person who knows, from years of first-hand experience, how relationship managers and treasury officers actually serve their clients, where the revenue lives in a commercial banking book, and what an insight has to look like before a banker will act on it. This is a product subject matter expert role first and foremost: you will shape the data and insights modules that Codat's Banking Context Engine generates for banker users. You will use these expertise in front of prospects and customers, working alongside our GTM team in conversations with some of the largest commercial banks in North America and the UK.
The Role
This role sits at a unique intersection between Product, GTM and strategy covering the following broad intersection.
Design actionable insights: Work hand in hand with Product Managers to deliver the substance of the insight modules our Banking Context Engine delivers — defining what signals matter across Treasury & Payments, FX, supply chain finance, working capital and lending, what a banker needs to see to act, and what \"actionable\" genuinely means in the context of a client conversation. You'll separate insights that generate fee revenue and deepen relationships from noise that erodes banker trust.
Be the voice of the banker: Bring the daily reality of commercial bankers and treasury management officers into every product decision — coverage models, portfolio reviews, pre-call preparation, annual pricing conversations, cross-sell motions — so that what we build fits how banks actually work rather than how outsiders imagine they work.
Consult into the product build: Actively collaborate with our product and engineering teams as they build the Banking Context Engine and its MCP integration with Microsoft Copilot, translating banking domain knowledge into concrete requirements, evaluation criteria, and quality bars for generated insights.
Support GTM as the banking SME: Join sales conversations, demos, and executive meetings with prospect and customer banks as the credible domain expert in the room. Support discovery by speaking the language of bank product heads, treasury management leaders and commercial banking executives, and help our GTM team pressure-test messaging against how banks really buy.
Sharpen how banks measure value: Help build the ROI narratives that connect Codat's insights to outcomes banks care about — fee revenue growth from the existing book, expanded coverage without added headcount, and client retention against fintech encroachment.
Build institutional knowledge: Codify what you know — banker workflows, product economics, org structures, buying dynamics — into reusable assets that raise the banking fluency of the whole company.
What you'll bring to the team
Substantial first-hand experience inside commercial banking — as a treasury management officer or sales/product lead, commercial relationship manager, or commercial product manager serving mid-market and SMB clients
A working command of how commercial banks make money from operating relationships: treasury and payments fee structures, deposit economics, FX, card, supply chain finance and working capital products, and how these are priced, bundled and cross-sold
Deep familiarity with how banks organize coverage — RM and TMO territory models, portfolio segmentation, call planning, and the practical constraints that determine whether a client gets proactive advice or none at all
The ability to translate domain expertise into product requirements: you can tell a product team precisely why an insight will or won't land with a banker, and articulate what would change that
Credibility and composure in front of senior bank executives — you have sat across from (or been) the heads of treasury management, commercial banking or product at a significant institution, and can hold that room
Genuine curiosity about AI-assisted banking workflows — familiarity with copilot-style tools (e.g. Microsoft Copilot) and an informed point of view on where AI genuinely helps bankers.
Comfort operating in a fast-moving scale-up with significant opportunity to influence our future success
About Codat
Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.
Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.
The Role
We're building the AI Assistant for commercial bankers. Relationship managers, treasury and credit teams use it to understand their business customers and act faster, and you'll be right in the middle of building it.
This is a hands-on job for people who like building and want to stay as an individual contributor. You'll design and ship the things the assistant depends on, and they'll still be yours once they're live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.
What You'll Work On
The pipelines the assistant draws on, from raw source data to the numbers it quotes. You'll build them and keep them running.
The layers on top: metrics, definitions and the relationships between them, so a pile of financial data becomes something the assistant can reason over.
Testing and observability. We want what we ship to get more reliable, and we want the people around you to get better at it too.
You'll Probably Have
Built pipelines and production systems from scratch, writing the code yourself rather than mostly configuring managed services. If we ask about the hardest logic you've written, you'll have a story ready.
Good Python habits. Your tests are the kind someone else would be glad to inherit.
Real depth in a few of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal and dbt, and opinions about which ones annoyed you.
Time with CI/CD, Docker and cloud infrastructure. Even better if you set them up for a team.
An interest in the business behind the data. If the data says the request is wrong, you'll say so.
AI already built into how you work. You can show us where it saved you real time, or got you up to speed on something new.
Nice to have: some experience getting data ready for LLMs, like semantic layers, text-to-SQL or retrieval.
How We Work
We're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.
There’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point.
","descriptionPlain":"About Codat\n\nCodat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.\n\nFounded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.\n\nThe Role\n\nWe're building the AI Assistant for commercial bankers. Relationship managers, treasury and credit teams use it to understand their business customers and act faster, and you'll be right in the middle of building it.\n\nThis is a hands-on job for people who like building and want to stay as an individual contributor. You'll design and ship the things the assistant depends on, and they'll still be yours once they're live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.\n\nWhat You'll Work On\n\n - The pipelines the assistant draws on, from raw source data to the numbers it quotes. You'll build them and keep them running.\n\n - The layers on top: metrics, definitions and the relationships between them, so a pile of financial data becomes something the assistant can reason over.\n\n - Testing and observability. We want what we ship to get more reliable, and we want the people around you to get better at it too.\n\nYou'll Probably Have\n\n - Built pipelines and production systems from scratch, writing the code yourself rather than mostly configuring managed services. If we ask about the hardest logic you've written, you'll have a story ready.\n\n - Good Python habits. Your tests are the kind someone else would be glad to inherit.\n\n - Real depth in a few of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal and dbt, and opinions about which ones annoyed you.\n\n - Time with CI/CD, Docker and cloud infrastructure. Even better if you set them up for a team.\n\n - An interest in the business behind the data. If the data says the request is wrong, you'll say so.\n\n - AI already built into how you work. You can show us where it saved you real time, or got you up to speed on something new.\n\n - Nice to have: some experience getting data ready for LLMs, like semantic layers, text-to-SQL or retrieval.\n\nHow We Work\n\nWe're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.\n\nThere’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point."},{"id":"33d61818-455c-4430-af32-24ab58d46756","title":"Senior Data Engineer","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"Remote","secondaryLocations":[],"publishedAt":"2026-07-27T11:22:18.964+00:00","isListed":true,"isRemote":true,"workplaceType":"Remote","address":{"postalAddress":{"addressCountry":"United Kingdom"}},"jobUrl":"https://jobs.ashbyhq.com/codat/33d61818-455c-4430-af32-24ab58d46756","applyUrl":"https://jobs.ashbyhq.com/codat/33d61818-455c-4430-af32-24ab58d46756/application","descriptionHtml":"About Codat
Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.
Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.
The Role
We're building the AI Assistant for commercial bankers. Relationship managers, treasury and credit teams use it to understand their business customers and act faster, and you'll be right in the middle of building it.
This is a hands-on job for people who like building and want to stay as an individual contributor. You'll design and ship the things the assistant depends on, and they'll still be yours once they're live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.
What You'll Work On
The pipelines the assistant draws on, from raw source data to the numbers it quotes. You'll build them and keep them running.
The layers on top: metrics, definitions and the relationships between them, so a pile of financial data becomes something the assistant can reason over.
Testing and observability. We want what we ship to get more reliable, and we want the people around you to get better at it too.
You'll Probably Have
Built pipelines and production systems from scratch, writing the code yourself rather than mostly configuring managed services. If we ask about the hardest logic you've written, you'll have a story ready.
Good Python habits. Your tests are the kind someone else would be glad to inherit.
Real depth in a few of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal and dbt, and opinions about which ones annoyed you.
Time with CI/CD, Docker and cloud infrastructure. Even better if you set them up for a team.
An interest in the business behind the data. If the data says the request is wrong, you'll say so.
AI already built into how you work. You can show us where it saved you real time, or got you up to speed on something new.
Nice to have: some experience getting data ready for LLMs, like semantic layers, text-to-SQL or retrieval.
How We Work
We're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.
There’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point.
","descriptionPlain":"About Codat\n\nCodat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.\n\nFounded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.\n\nThe Role\n\nWe're building the AI Assistant for commercial bankers. Relationship managers, treasury and credit teams use it to understand their business customers and act faster, and you'll be right in the middle of building it.\n\nThis is a hands-on job for people who like building and want to stay as an individual contributor. You'll design and ship the things the assistant depends on, and they'll still be yours once they're live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.\n\nWhat You'll Work On\n\n - The pipelines the assistant draws on, from raw source data to the numbers it quotes. You'll build them and keep them running.\n\n - The layers on top: metrics, definitions and the relationships between them, so a pile of financial data becomes something the assistant can reason over.\n\n - Testing and observability. We want what we ship to get more reliable, and we want the people around you to get better at it too.\n\nYou'll Probably Have\n\n - Built pipelines and production systems from scratch, writing the code yourself rather than mostly configuring managed services. If we ask about the hardest logic you've written, you'll have a story ready.\n\n - Good Python habits. Your tests are the kind someone else would be glad to inherit.\n\n - Real depth in a few of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal and dbt, and opinions about which ones annoyed you.\n\n - Time with CI/CD, Docker and cloud infrastructure. Even better if you set them up for a team.\n\n - An interest in the business behind the data. If the data says the request is wrong, you'll say so.\n\n - AI already built into how you work. You can show us where it saved you real time, or got you up to speed on something new.\n\n - Nice to have: some experience getting data ready for LLMs, like semantic layers, text-to-SQL or retrieval.\n\nHow We Work\n\nWe're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.\n\nThere’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point."},{"id":"17fa6713-6180-4a33-bac8-7405dba7c0a2","title":"Senior Software Engineer (AI/Data)","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"London","secondaryLocations":[],"publishedAt":"2026-10-28T17:02:33.946+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressRegion":"Greater London","addressCountry":"United Kingdom","addressLocality":"London"}},"jobUrl":"https://jobs.ashbyhq.com/codat/17fa6713-6180-4a33-bac8-7405dba7c0a2","applyUrl":"https://jobs.ashbyhq.com/codat/17fa6713-6180-4a33-bac8-7405dba7c0a2/application","descriptionHtml":"About Codat
Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.
Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.
We're building the Advisory Intelligence Platform: Codat's client intelligence delivered inside the assistant a banker already uses (Microsoft Copilot first). Ask it anything about a client and it answers from the client's real books. Don't ask, and it still tells you what's changed, why it matters and what to do about it. Under it we're building the semantic layer, so every answer uses the same definitions, the signal layer that spots what's changed for a client, and the reasoning layer, a Codat-side LLM that turns those signals into advice. The first bank goes live this year. You'll be building the data and reasoning behind all of it.
This is a hands-on job for software engineers who want to build the systems a model reasons over, not configure a warehouse. Codat is AI-first, not AI bolted onto SaaS. The product lives inside the AI assistant our clients already use, and we build it the same way: agents do the repetitive work, engineers do the hard parts. You'll build it and you'll run it. Nobody hands it over the wall once it's live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.
The semantic layer. Measures, dimensions and definitions the model is allowed to use, so every answer it gives is the same answer. Yours to grow, persona by persona.
The signal layer. Detecting what's changed for a client and surfacing it before the banker asks. You'll be designing and building this.
The reasoning layer. A Codat-side LLM that turns signals into advice. Our calculations are the ground truth it reasons over. You own the calculations the model relies on, and the names, definitions and structure that let it pick the right one.
The agents and MCP (Model Context Protocol) servers behind the assistant. What tools it has, how they're built, how they fail, and the evals that prove they work.
Everything from raw source data to the number the assistant quotes: the pipelines, services and data models, with the testing and observability to prove the number is right and the evals that catch the model quoting the wrong one.
Built production systems from scratch, writing the code yourself rather than configuring managed services. You think about interfaces, failure modes and how the next person will change your code. If we ask about the hardest system you've built, you'll have a story ready.
Served data to an LLM and seen where it goes wrong: ambiguous definitions, poor retrieval, no evals. You know why \"the model will work it out\" isn't a data strategy.
Worked with data along the way, and liked it. You may never have been called a data engineer. You've built things that move, shape and serve data, and you know how they break, and how a model breaks on top of them.
Solid C#, Python or another backend language. Your code comes with tests, and you leave things tidier than you found them.
Depth in at least one of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal or dbt. Bonus if you've shaped any of them into something a model can query, through a metrics layer, metric views or an MCP server.
Time with CI/CD, Docker, cloud infrastructure (we're on Azure) and observability. Even better if you set them up for a team.
An interest in the business behind the data. If someone asks for a number the data can't support, you'll say so, and you'll build the guard-rail so the model says so too.
Treated your own workflow as something to engineer. AI has already taken over the repetitive parts of it, and you're the person others ask how to do the same.
We're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.
There’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point.
","descriptionPlain":"About Codat\n\nCodat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.\n\nFounded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.\n\n\nTHE ROLE\n\nWe're building the Advisory Intelligence Platform: Codat's client intelligence delivered inside the assistant a banker already uses (Microsoft Copilot first). Ask it anything about a client and it answers from the client's real books. Don't ask, and it still tells you what's changed, why it matters and what to do about it. Under it we're building the semantic layer, so every answer uses the same definitions, the signal layer that spots what's changed for a client, and the reasoning layer, a Codat-side LLM that turns those signals into advice. The first bank goes live this year. You'll be building the data and reasoning behind all of it.\n\nThis is a hands-on job for software engineers who want to build the systems a model reasons over, not configure a warehouse. Codat is AI-first, not AI bolted onto SaaS. The product lives inside the AI assistant our clients already use, and we build it the same way: agents do the repetitive work, engineers do the hard parts. You'll build it and you'll run it. Nobody hands it over the wall once it's live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.\n\n\n\n\nWHAT YOU'LL WORK ON\n\n - The semantic layer. Measures, dimensions and definitions the model is allowed to use, so every answer it gives is the same answer. Yours to grow, persona by persona.\n\n - The signal layer. Detecting what's changed for a client and surfacing it before the banker asks. You'll be designing and building this.\n\n - The reasoning layer. A Codat-side LLM that turns signals into advice. Our calculations are the ground truth it reasons over. You own the calculations the model relies on, and the names, definitions and structure that let it pick the right one.\n\n - The agents and MCP (Model Context Protocol) servers behind the assistant. What tools it has, how they're built, how they fail, and the evals that prove they work.\n\n - Everything from raw source data to the number the assistant quotes: the pipelines, services and data models, with the testing and observability to prove the number is right and the evals that catch the model quoting the wrong one.\n\n\nYOU'LL PROBABLY HAVE\n\n - Built production systems from scratch, writing the code yourself rather than configuring managed services. You think about interfaces, failure modes and how the next person will change your code. If we ask about the hardest system you've built, you'll have a story ready.\n\n - Served data to an LLM and seen where it goes wrong: ambiguous definitions, poor retrieval, no evals. You know why \"the model will work it out\" isn't a data strategy.\n\n - Worked with data along the way, and liked it. You may never have been called a data engineer. You've built things that move, shape and serve data, and you know how they break, and how a model breaks on top of them.\n\n - Solid C#, Python or another backend language. Your code comes with tests, and you leave things tidier than you found them.\n\n - Depth in at least one of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal or dbt. Bonus if you've shaped any of them into something a model can query, through a metrics layer, metric views or an MCP server.\n\n - Time with CI/CD, Docker, cloud infrastructure (we're on Azure) and observability. Even better if you set them up for a team.\n\n - An interest in the business behind the data. If someone asks for a number the data can't support, you'll say so, and you'll build the guard-rail so the model says so too.\n\n - Treated your own workflow as something to engineer. AI has already taken over the repetitive parts of it, and you're the person others ask how to do the same.\n \n \n\n\nHOW WE WORK\n\nWe're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.\n\nThere’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point.\n\n"},{"id":"e0a0d43e-c68c-462b-bee4-ab2fabaf7147","title":"Senior Software Engineer (AI/Data)","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"Remote","secondaryLocations":[],"publishedAt":"2026-10-28T17:02:36.011+00:00","isListed":true,"isRemote":true,"workplaceType":"Remote","address":{"postalAddress":{"addressCountry":"United Kingdom"}},"jobUrl":"https://jobs.ashbyhq.com/codat/e0a0d43e-c68c-462b-bee4-ab2fabaf7147","applyUrl":"https://jobs.ashbyhq.com/codat/e0a0d43e-c68c-462b-bee4-ab2fabaf7147/application","descriptionHtml":"About Codat
Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.
Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.
We're building the Advisory Intelligence Platform: Codat's client intelligence delivered inside the assistant a banker already uses (Microsoft Copilot first). Ask it anything about a client and it answers from the client's real books. Don't ask, and it still tells you what's changed, why it matters and what to do about it. Under it we're building the semantic layer, so every answer uses the same definitions, the signal layer that spots what's changed for a client, and the reasoning layer, a Codat-side LLM that turns those signals into advice. The first bank goes live this year. You'll be building the data and reasoning behind all of it.
This is a hands-on job for software engineers who want to build the systems a model reasons over, not configure a warehouse. Codat is AI-first, not AI bolted onto SaaS. The product lives inside the AI assistant our clients already use, and we build it the same way: agents do the repetitive work, engineers do the hard parts. You'll build it and you'll run it. Nobody hands it over the wall once it's live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.
The semantic layer. Measures, dimensions and definitions the model is allowed to use, so every answer it gives is the same answer. Yours to grow, persona by persona.
The signal layer. Detecting what's changed for a client and surfacing it before the banker asks. You'll be designing and building this.
The reasoning layer. A Codat-side LLM that turns signals into advice. Our calculations are the ground truth it reasons over. You own the calculations the model relies on, and the names, definitions and structure that let it pick the right one.
The agents and MCP (Model Context Protocol) servers behind the assistant. What tools it has, how they're built, how they fail, and the evals that prove they work.
Everything from raw source data to the number the assistant quotes: the pipelines, services and data models, with the testing and observability to prove the number is right and the evals that catch the model quoting the wrong one.
Built production systems from scratch, writing the code yourself rather than configuring managed services. You think about interfaces, failure modes and how the next person will change your code. If we ask about the hardest system you've built, you'll have a story ready.
Served data to an LLM and seen where it goes wrong: ambiguous definitions, poor retrieval, no evals. You know why \"the model will work it out\" isn't a data strategy.
Worked with data along the way, and liked it. You may never have been called a data engineer. You've built things that move, shape and serve data, and you know how they break, and how a model breaks on top of them.
Solid C#, Python or another backend language. Your code comes with tests, and you leave things tidier than you found them.
Depth in at least one of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal or dbt. Bonus if you've shaped any of them into something a model can query, through a metrics layer, metric views or an MCP server.
Time with CI/CD, Docker, cloud infrastructure (we're on Azure) and observability. Even better if you set them up for a team.
An interest in the business behind the data. If someone asks for a number the data can't support, you'll say so, and you'll build the guard-rail so the model says so too.
Treated your own workflow as something to engineer. AI has already taken over the repetitive parts of it, and you're the person others ask how to do the same.
We're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.
There’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point.
","descriptionPlain":"About Codat\n\nCodat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.\n\nFounded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.\n\n\nTHE ROLE\n\nWe're building the Advisory Intelligence Platform: Codat's client intelligence delivered inside the assistant a banker already uses (Microsoft Copilot first). Ask it anything about a client and it answers from the client's real books. Don't ask, and it still tells you what's changed, why it matters and what to do about it. Under it we're building the semantic layer, so every answer uses the same definitions, the signal layer that spots what's changed for a client, and the reasoning layer, a Codat-side LLM that turns those signals into advice. The first bank goes live this year. You'll be building the data and reasoning behind all of it.\n\nThis is a hands-on job for software engineers who want to build the systems a model reasons over, not configure a warehouse. Codat is AI-first, not AI bolted onto SaaS. The product lives inside the AI assistant our clients already use, and we build it the same way: agents do the repetitive work, engineers do the hard parts. You'll build it and you'll run it. Nobody hands it over the wall once it's live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.\n\n\n\n\nWHAT YOU'LL WORK ON\n\n - The semantic layer. Measures, dimensions and definitions the model is allowed to use, so every answer it gives is the same answer. Yours to grow, persona by persona.\n\n - The signal layer. Detecting what's changed for a client and surfacing it before the banker asks. You'll be designing and building this.\n\n - The reasoning layer. A Codat-side LLM that turns signals into advice. Our calculations are the ground truth it reasons over. You own the calculations the model relies on, and the names, definitions and structure that let it pick the right one.\n\n - The agents and MCP (Model Context Protocol) servers behind the assistant. What tools it has, how they're built, how they fail, and the evals that prove they work.\n\n - Everything from raw source data to the number the assistant quotes: the pipelines, services and data models, with the testing and observability to prove the number is right and the evals that catch the model quoting the wrong one.\n\n\nYOU'LL PROBABLY HAVE\n\n - Built production systems from scratch, writing the code yourself rather than configuring managed services. You think about interfaces, failure modes and how the next person will change your code. If we ask about the hardest system you've built, you'll have a story ready.\n\n - Served data to an LLM and seen where it goes wrong: ambiguous definitions, poor retrieval, no evals. You know why \"the model will work it out\" isn't a data strategy.\n\n - Worked with data along the way, and liked it. You may never have been called a data engineer. You've built things that move, shape and serve data, and you know how they break, and how a model breaks on top of them.\n\n - Solid C#, Python or another backend language. Your code comes with tests, and you leave things tidier than you found them.\n\n - Depth in at least one of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal or dbt. Bonus if you've shaped any of them into something a model can query, through a metrics layer, metric views or an MCP server.\n\n - Time with CI/CD, Docker, cloud infrastructure (we're on Azure) and observability. Even better if you set them up for a team.\n\n - An interest in the business behind the data. If someone asks for a number the data can't support, you'll say so, and you'll build the guard-rail so the model says so too.\n\n - Treated your own workflow as something to engineer. AI has already taken over the repetitive parts of it, and you're the person others ask how to do the same.\n\n\nHOW WE WORK\n\nWe're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.\n\nThere’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point.\n\n"}],"apiVersion":"1"}