Analytics Engineer

About InvestEngine

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.

About the role

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.

You won’t just write dbt models in isolation. You’ll work directly with stakeholders across the business — Product, Engineering, Risk, Finance, Investment, Marketing — to understand the problems they’re actually trying to solve, then design and ship the pipelines, models, and semantic layer that solve them. You’ll also help shape how we use AI-enabled tooling to make the whole analytics function faster, and how we structure our data so that AI tooling can be trusted to answer questions about it.

We’re actively modernising our data stack, and we want the person in this role to shape it. Some of it is in place, some of it is being rebuilt, and some of it hasn’t been decided yet. If you have informed opinions about orchestration, ingestion, testing, semantic layers, or BI — we want to hear them.

Who thrives here

We’ve pitched this at mid to senior level, but the honest position is that the level matters less than the appetite. The ultimate test is hunger, and the desire to shape this space and have real impact. If you’re technically strong but hungrier than your years of experience suggest, apply anyway — we’d rather have someone who wants to own this than someone who has simply done it before somewhere bigger.

Beyond that, this role suits someone who:

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.

Our values

Everyone at InvestEngine is expected to live these, and this role will be assessed against all five:

Act like an owner — you take responsibility for outcomes, not just tasks

Keep improving. Stay curious. — you push your own standards and the team’s, and you’re genuinely curious about better ways to work

Achieve more together — you make the people around you (business stakeholders included) more effective, not just yourself

Speak up. Share openly. — you flag risks, disagreements, and better ideas rather than sitting on them

Put customers first. Create real value. — you build things because they solve a real problem, not because they’re interesting to build

What you’ll do

Analytics engineering & the semantic layer

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

Building the AI context layer

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

Data engineering & the platform

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

Warehouse cost & performance

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

Data quality & observability

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

Upstream influence

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

Business partnership

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

Governance

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