About
We're a ~200-person, AI-first fintech. We move fast, we expect people to own things end to end, and we don't have a lot of specialist support infrastructure to hide behind — if you spot a gap, you're expected to close it, not escalate it and wait. We're hiring our first dedicated Analytics Engineer at InvestEngine. This is a foundational hire: you'll sit at the intersection of data engineering, analytics engineering, and business partnership — turning raw, disparate data into governed, trustworthy models that the rest of the company makes decisions on.
Responsibilities
•Design, build, and maintain dbt Core models that transform raw data into clean, well-tested, well-documented datasets
•Own and evolve our semantic layer — consistent metric definitions, business logic, and naming that the whole company can trust and query against
•Establish and enforce data modelling standards, testing practices, and documentation as the function scales
•Evaluate and help roll out modern BI tooling — experience with Lightdash, Omni, Looker, or Metabase all translates well — to make metrics genuinely self-serve for non-technical stakeholders
•Structure our models, metric definitions, documentation, and lineage so they're machine-readable, not just human-readable — because our semantic layer is what AI tooling reasons over
•Make AI-driven analytics reliable: if two people (or two agents) ask the same business question, they should get the same answer, because the definition lives in one governed place
•Treat naming, documentation, and metric definitions as first-class engineering deliverables rather than afterthoughts, since they directly determine how trustworthy our AI-enabled BI is
•Use Claude to accelerate development — model scaffolding, code review, documentation, QA, and pipeline debugging — and Notion AI to keep documentation and runbooks current and genuinely useful
•Bring a point of view on where AI can responsibly speed up analytics engineering, and help the wider team adopt it well
•Build and maintain ingestion and transformation pipelines across our stack. We use a modern data stack, such as AWS, Redshift, and Airflow
•Write production-quality Python for extraction, transformation, and automation tasks that fall outside dbt's remit
•Help us modernise: we're actively evolving this stack and expect you to challenge and improve it, not just operate it
•Own the performance and cost profile of our Redshift warehouse — you should be able to read a query plan, choose sensible sort and distribution keys, and design incremental models without being asked
•Spot and act on opportunities to reduce complexity and cost across our models and pipelines, rather than letting spend and technical debt accumulate quietly
•Make deliberate trade-offs between freshness, cost, and complexity — and explain those trade-offs to the business in terms they care about
•Champion analytics engineering best practice across the company: testing, version control, code review, CI, and documentation applied to data the same way engineering applies them to software
•Build and own data quality, freshness, and pipeline monitoring so problems are caught by us before they're caught by a stakeholder looking at a dashboard
•Set the standard for what "trustworthy data" means here, and hold the line on it as the volume of models and requests grows
•Work with our engineering teams to make sure application and event design accommodates reporting and analytics requirements before data reaches the warehouse — good analytics starts at the source, not in the transformation layer
•Influence payload and schema design for new services and product features, so we're not permanently reverse-engineering business meaning out of operational tables
•Support analysts and business users with automation, tooling, and data engineering expertise, and provide training and guidance on how to use our data well
•Work directly with stakeholders to understand their problems, not just their requested outputs — translate ambiguous business questions into pipeline and modelling requirements
•Act as a trusted advisor on what's possible with our data, and push back constructively when a request doesn't solve the underlying problem
•Support financial, operational, and regulatory reporting needs appropriate to a regulated investment platform
•Apply our data classification and access model when onboarding sources: identify PII (including in free-text fields), decide what belongs in the sanitised analytics layer versus a restricted dataset, and document the outcome
Requirements
•Operates well with ambiguity and limited structure. You'll be the first analytics engineer here — no existing playbook, no dedicated data engineering team to lean on, no established BI function to slot into. You'll be building a lot of it as you go.
•Wants a small, fast company on purpose — not just escaping bureaucracy elsewhere, but genuinely drawn to the trade-off: more ownership and visibility, less process and hand-holding.
•Lives in the tools, not in email chains. You default to GitHub, dbt, Slack/Notion, and automation over status meetings and Word docs.
•Can point to things they've actually built and shipped, not just designs or concepts they contributed to.
•Already uses AI to do the job better — Claude, Copilot, or similar — and is curious about where it can go further.
•Learns fast. We care more about how quickly you close a knowledge gap than how complete your CV looks today.
•Works well directly with Product, Marketing, Operations, and Engineering, not just with other analysts — this role sits right at that boundary.
•Hands-on production experience with dbt (we run dbt Core) — this is a hard requirement
•Strong SQL expertise
•Solid Python for data engineering and automation
•Working knowledge of AWS data services
•Experience with GitHub and GitHub Actions (or equivalent CI/CD tooling) for testing and deploying data code
•Experience with a modern BI tool (e.g., Lightdash, Omni, Looker, or Metabase), and a genuine interest in semantic layers (e.g., MetricFlow, Cube Core) and self-serve analytics
•Comfortable using AI tools as part of your day-to-day engineering workflow
•Strong communication — you can sit with a non-technical stakeholder, understand their real problem, and explain trade-offs in plain language
•Ownership: you're comfortable being the first and only person in this role, and building things properly from scratch
Nice to have
•Airflow or similar orchestration tools, and familiarity with data ingestion tools such as dlt or Airbyte
•Warehouse cost and performance tuning, particularly on Redshift
•Fintech, investment management, or another regulated industry
•Handling PII responsibly in an analytics environment — sanitised vs. restricted datasets, role-based access tiers, automated PII detection
•Experience with Notion AI or Asana automations
Conditions
No conditions stated in the posting.
