As our Senior Analytics Engineer for Operations, you'll own that data end to end, the model behind it, the metrics it produces, and the trust the business places in both.

This role isn't just "build the metric, ship the dashboard." We want someone who looks past the number to the process behind it, where it's manual, where it's fragile, where the right tool removes the work instead of just reporting on it. You'll have a direct line to influencing how those processes and the surrounding product actually work, not just how they're measured.

You'll sit embedded inside Operations, partnering with the TRI (Trust & Risk Intelligence) domain wherever the two overlap, while working inside the Data & Analytics team's AE guild — so you'll have the tools and the context to actually contribute, not just execute.

What You Will Be Doing

Own the Operations data model — reconciliation, treasury, payments, lifecycle, and onboarding — turning raw operational events into documented, trustworthy data products on the DWH
Partner directly with Operations stakeholders on day-to-day questions, ad hoc requests, and recurring reporting, acting as their primary data point of contact — while pushing routine, repeatable questions toward self-service instead of answering the same thing by hand every time
Look past the metric to the process behind it — understand how everything actually works end to end, spot where manual effort or fragile handoffs are the real problem, and identify which tools (not just which query) would solve it
Define and maintain key metrics for reconciliation accuracy, treasury positions, payment success/failure rates, and onboarding conversion, working within the team's central metric architecture
Participate in data quality and governance practices for the domain
Bring process and product ideas to the table directly — you're close enough to the operational reality to see what should change, and this role expects you to say so and help make it happen, not just report the numbers upward
Your First Six Months

First month Learn the Operations domain and how it currently works and reports, end to end. Meet your stakeholders and your teammates. Ship a first small, well-scoped fix or data product from the existing backlog to get real signal into the system quickly

First 3 months Own at least one Operations sub-domain's metrics end to end. Start contributing to the shared Risk/TRI conversations, and bring your first concrete process or tooling suggestion to a stakeholder — something you noticed while getting close to the actual workflow

First 6 months Operations stakeholders treat you as their data partner, not the new person. Routine checks are answered by self-service — what reaches you is what actually needs a person. You hold real, independent ownership over the data and workflows under your supervision

Who You Are

You've worked as an analytics engineer or senior analyst on technical projects involving data modeling on a modern data platform (Databricks, Spark, or dbt) — not just querying data others built
You write excellent SQL for analytical work, and you can pick up Python for anything SQL doesn't cover well
You're confident gathering requirements directly from operational stakeholders and turning ambiguous asks into clean, documented metrics
You default to precision over speed when the two are in tension — Operations data feeds regulatory and financial reporting, and being wrong quietly is worse than being slow
You can hold two stakeholders' definitions of "the same" metric and reconcile them, rather than picking whichever is more convenient
You think past the metric to the process it describes — you want to understand how the domain's tasks actually get done, spot which tool or automation would remove real work rather than just report on it, and you push for that change rather than waiting to be asked
You have a high level of autonomy and self-direction — you're the one who decides what needs fixing, not just how to measure it
AI-assisted workflows and tools are just part of how you work, not a novelty

Nice to Have

Background in banking, fintech, treasury, or payments operations
Experience working across multiple stakeholder groups with overlapping but not identical needs
Familiarity with regulatory or financial reporting data requirements
Occasional exposure to BI-layer reporting (PSM, lifecycle, or similar) when it intersects with your data products