Analytics engineer in fintech

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.

• 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

• 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
• Будет плюсом: 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

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