Senior Analytics Engineer; Antifraud

Sumsub is the first AI-powered trust infrastructure for compliance operations  connects identity and business verification, fraud prevention, transaction monitoring, and risk workflows, helping teams reduce manual work and enter new markets.

Trusted by over 4,000 clients across financial services, crypto, mobility, trading, marketplaces, education, and iGaming, including Bybit, Bitpanda, Wirex, Avis, Vodafone, Duolingo, and Kaizen Gaming.

Sumsub is recognized as a Great Place To Work® in the US and rated 4.5 out of 5 on Glassdoor. Our 1,000+ people around the world back it up, too: 92% are proud to be Sumsubers, and 90% say their work is interesting and challenging.

We are looking for a Senior Analytics Engineer to strengthen the data foundation behind Sumsub's fraud prevention capabilities. You will join the Analytics team and report to the Head of Product Analytics.

The role combines data modeling with hands‑on analysis. You will design and evolve the data models that anti‑fraud analytics relies on, then use them to evaluate how our fraud prevention tools perform and turn the results into insights for Product and for clients.

This is a hands‑on role for someone who enjoys building the model and then being the first to ask hard questions of it.

• Design, build and evolve core data models for fraud prevention analytics in Click House and dbt.

• Create reliable, well‑documented datasets that make detection performance easy to measure and compare.

• Help define shared metrics and standards so different teams work from the same numbers.

• Own data quality: tests, documentation, lineage and monitoring.

• Partner with Data Engineering, Product and anti‑fraud experts to bring new data sources into the model.

• Analyse the performance of anti‑fraud tools across products, clients and fraud scenarios.

• Develop repeatable approaches for assessing changes to detection logic.

• Turn findings into clear, actionable recommendations for Product and for clients.

• Support complex, data‑intensive client cases when needed.

• 4+ years in analytics engineering, data engineering or product analytics, with data modeling as a core part of your work.

• Expert SQL and strong data modeling skills: dimensional modeling, event data, slowly changing dimensions, incremental models, and clear grain for every table.

• Production experience with dbt (or a comparable tool): tests, documentation, incremental materialisations, code review.

• Hands‑on experience with Click House or another columnar warehouse, and with large event‑level datasets.

• Strong Python for analysis and data work.

• Solid grasp of detection‑quality evaluation: confusion matrix, precision/recall, PR and ROC curves, threshold trade‑offs and the cost of errors.

• Strong judgement with imperfect data: you spot bias, leakage and misleading metrics before they reach a dashboard.

• Ability to explain findings to product managers and clients through clear logic, numbers and concrete examples.

• Hands‑on experience with AI tools in analytics and data engineering work, or a strong readiness to make them a core part of how you build and analyse.

• Fluency in English.

• Remote‑first, trust‑based culture. Work from the place that works best for you. No mandatory office days, no attendance trackers. In some locations, we provide offices or coworking spaces, but the choice is yours.

• True flexibility. We do not fix you to a 9‑to‑5 schedule. You can adjust your working hours when needed, as long as your day stays productive and in sync with the team.

• Extra time off. Your birthday is a holiday here. Add to that 10 personal days each year, seven sick days without paperwork, and extra time to enjoy…