Senior Data Engineer

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ABOUT BUREAU

Bureau is a unified risk decisioning platform for Compliance, Fraud, and Transaction risks. Our platform is a single decision-making engine, powered by a 1 billion+ identity knowledge graph. Over 150 Banks, fintechs, retailers, and digital platforms use Bureau to verify identities faster and stop fraud earlier globally.

Bureau has raised $50M+ from renowned Silicon Valley and global investors including Sorenson Capital and PayPal Ventures and is expanding rapidly from APAC to Americas, Europe, and beyond.

WHY BUREAU?

Bureau is building the infrastructure that makes digital identities and transactions safe and trustworthy for billions of people. The mission is big, the problems are complex, and the impact is real.

We hire people who want that level of responsibility. People who move fast, build systems from scratch, and care deeply about turning strategy into execution. If you want predictability or narrow scope, this won't be your place. If you want to shape how a scaling global company operates—keep reading.

About the Role - Senior Data Engineer

As a Senior Data Engineer at Bureau, you will design and own the data platform that fuels our fraud detection and identity intelligence products. You'll work at serious scale, high-throughput streaming signals, a growing cloud data lake, and low-latency serving requirements where it directly impacts fraud detection.

You'll partner closely with data scientists, ML engineers, and product teams to make sure the right data is available, trustworthy, and fast, for research, reporting, model training, and real-time decisioning alike.

What You'll Do

- Design and architect Bureau's cloud data lake and lakehouse, the single source of truth for identity signals, risk events, and decision outcomes across all our products

- Build and operate scalable batch and streaming pipelines that ingest, clean, transform, and aggregate data from disparate sources, device SDKs, APIs, third-party data partners, and internal services

- Develop and maintain orchestration workflows (Airflow) that power research, reporting, compliance analytics, and ML model training and feature pipelines

- Own the reliability, observability, and cost-efficiency of production data infrastructure , with monitoring, alerting, and SLAs appropriate for a system that makes real-time risk decisions

- Collaborate with data science and ML teams to design feature stores and data pipelines that shorten the path from raw signal to deployed model

- Champion data engineering best practices, data quality, schema governance, testing, documentation, and mentor engineers across the team

- Contribute to graph-based fraud intelligence: modeling identity networks and fraud rings using graph databases

What You'll Bring

Must have