The forward-deployed engineer who owns the data end to end: tested pipelines, models people can query, and a platform your team can run, built inside your team and handed over documented.
Why this role exists
A dashboard is only as good as the pipelines behind it. When nobody owns ingestion, modelling and testing end to end, numbers drift, costs creep and analysts spend their days firefighting instead of analysing.
Most teams do not need a data platform department. They need one senior engineer who owns the flows from source to decision, works inside their codebase, and leaves the platform running without us. That is exactly what this role is.
What this engineer owns
The engagement starts with a single senior engineer inside your team and grows only when the problem justifies it. These are the commitments behind the role; exact scope is agreed with your team.
Your data engineer takes a use case from raw source to a number someone trusts: ingestion, modelling, testing, serving and monitoring. They write the pipelines, own the failures, and stay answerable when a metric moves.
What to verify: Ask who owns a metric from source to dashboard, and who is called when a number looks wrong.
Schemas change, sources go down, upstream data arrives late. The pipelines are built to detect all of it, alert with context, and stop bad data before it spreads rather than quietly writing nulls.
What to verify: Ask what happens when an upstream schema changes, and how long until anyone notices.
Layered, documented models with clear definitions: one definition of active customer, one definition of revenue, tested and versioned. Analysts stop arguing about whose number is right.
What to verify: Ask how many definitions of key metrics exist, and where they are documented.
Freshness, volume, uniqueness and schema tests run on every load. A broken pipeline is caught by a test, not by a manager asking why the dashboard is empty.
What to verify: Ask which tests run on each load, and what the last data incident looked like.
Warehouse and pipeline spend is measured per workload, queries are tuned, and scheduled jobs do not grow unbounded. The engineer finds the expensive queries and fixes or retires them.
What to verify: Ask what the monthly data platform spend is, and what changed it most recently.
Every model, pipeline and decision lives in your repository with written rationale and lineage. When the engagement ends, your team runs, extends and debugs the platform without us in the loop.
What to verify: Ask where the pipelines are documented, and who on your side can change a model after handover.
How they work
The loop is simple and strict: ingest the data, model it with clear definitions, serve it through a quality gate, and monitor it with the tests that prove it is healthy.
AI in the engagement
AI is not a separate offering bolted on. It is part of how this engineer works, and the judgment they bring is part of what your team absorbs.
AI tooling speeds up boilerplate transformations, tests and documentation drafts. What ships is SQL and pipeline code the engineer has read, understood and taken responsibility for.
AI can draft a model quickly, but the engineer checks the source semantics, nullability and edge cases against the actual data before it serves anyone a number.
AI-assisted changes go through the same review, tests and CI as everything else. Nothing reaches production because it looked finished.
AI keeps column descriptions and lineage notes in draft; the engineer verifies them against the real behaviour so downstream users can trust what they read.
Working principles
Written scope, reviewed changes and visible trade-offs decide whether an embedded engineer actually works. We make those commitments explicit rather than implied.
The sources, deliverables and consumers are written down before the first commit, and changes to them are agreed, not assumed.
Every pipeline change goes through review and CI like the rest of the codebase. The engineer is a team member, not an outside black box.
When freshness, cost and correctness conflict, the engineer shows the options and lets you decide, rather than picking quietly.
You work with the same engineer for the engagement, not a rotating bench. Continuity is what makes judgment about your data compound.
Security & trust
This role touches your sources, your users and your customers' data. Access, data boundaries and credentials are agreed in writing before the first commit.
The engineer gets scoped access to the warehouses, schedulers and repositories the work needs, nothing more, and access is reviewed and revoked at the end of the engagement.
Development and testing use masked or synthetic data. Real customer data is never copied into a laptop, a notebook or a test warehouse.
Connection strings and service credentials live in your secret manager, never in code or chat, and are rotated when the engagement ends.
Pipeline runs, model changes and access grants are logged, so any change to a number can be traced to a person and a reason after the fact.
Working arrangements
The engineer works remotely inside your rituals, in your repositories and data platform, with warehouses and schedulers running on infrastructure you control.
The engineer joins your stand-ups, reviews and planning from remote, and works in your repositories and data platform as a team member would.
Warehouses, schedulers and storage stay yours, with your credentials and your logging. We do not route your data through infrastructure you have not approved.
What the engineer may access, what may leave your environment and what must never leave are agreed in writing before development starts.
For workshops with the teams who own the sources, or a critical migration, on-site time is arranged deliberately.
How it starts
A paid two-week sprint proves the working relationship before anything longer is discussed. If it does not show real progress, you walk away.
Week one starts with the real data: which sources exist, what breaks, which numbers nobody trusts, and who waits on what. The engineer sits with your analysts and traces the flows as they actually run.
One goal in writing: for example, one trusted metric rebuilt end to end with tested ingestion, a documented model and a dashboard that reads from it. Success criteria are agreed before building starts.
The engineer builds inside your platform, opens small reviewed changes, and shows you the pipelines and dashboards working, including what did not work, at the end of each week.
At the end of the sprint you see what shipped, the test results and the lineage, and decide together what comes next: extend, hand over or stop.
The major cloud warehouses and lakehouses, plus the orchestration and transformation tools commonly built around them. The engineer fits your existing stack rather than insisting on a migration.
Yes, and that is the default. They work inside your repository, follow your conventions and improve the code they find, rather than starting something parallel.
No. They build the foundations your analysts depend on and hand the models over documented, so your analysts spend their time on analysis instead of firefighting pipelines.
By modelling reality, not wishing it clean. Sources are profiled first, edge cases are handled explicitly, and the messy parts are documented so downstream users know what they are reading.
No, but someone on your side owns the outcome. The engineer works with your product or analytics owner, and hands the platform over with documentation, tests and a documented operating process.
The sprint is a fixed, paid engagement agreed before it starts, and its scope is written down. If it does not show real progress, you walk away with everything built so far in your repository.
Let's talk
Tell us about the sources, the consumers and the decisions the data feeds. We will help define a first sprint goal and the evidence needed to judge it.