Data Engineer

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.

What You'll Do

Build and maintain batch and streaming pipelines that ingest, clean, and transform data from device SDKs, internal services, partner APIs, and third-party data providers

Write and own backend services and RESTful APIs that serve data to internal teams and to real-time decisioning paths

Develop Airflow DAGs powering reporting, compliance analytics, model training, and feature pipelines, and keep them healthy day to day

Write Spark jobs and SQL transformations against our data lake, and tune them when they get slow or expensive

Add monitoring, alerting, and data quality checks to the pipelines you own so problems surface before a stakeholder notices

Debug production issues across the stack: a Kafka consumer lagging, a schema change breaking downstream, a query that got 10x slower after a data volume jump

Work with the infrastructure your pipelines and services run on — containers, deployments, cluster configs — and help keep it stable and cost-sane

Contribute to our identity graph work, helping model relationships between entities to surface fraud rings and hidden linkages

Write documentation and tests, participate in design reviews, and help keep our schemas and contracts sane as the system grows

What You'll Bring

Must have

1–3 years of professional software engineering experience, with meaningful exposure to data-intensive systems

Strong programming skills in Python, Java, or Scala, and the ability to write production-quality, tested code

Strong SQL: joins, window functions, aggregations, and enough of a mental model of query execution to know why something is slow

Working understanding of databases, including the difference between OLTP and OLAP systems and when each is appropriate

Hands-on experience with at least one distributed data processing framework (Spark preferred) or a genuine willingness to ramp up quickly

Experience building or maintaining backend services and REST APIs

Familiarity with a major cloud platform (AWS preferred) and core services like S3, EC2, and managed databases

Solid computer science fundamentals: data structures, concurrency, and the basics of distributed systems

Comfort with Git, code review, and CI/CD

Nice to have

Infrastructure and systems knowledge — Docker, Kubernetes, Terraform or similar IaC, and a working sense of how services get deployed, scaled, and monitored in production

Experience running or tuning distributed workloads: cluster sizing, resource tuning, or tracking down a bottleneck between compute and storage

Exposure to Kafka or MSK, or any streaming/event-driven system

Experience with Airflow or a similar orchestration tool

Exposure to EMR, Athena, ClickHouse, Databricks, or Snowflake

Awareness of lakehouse table formats such as Iceberg, Delta Lake, or Hudi

Familiarity with observability tooling — Prometheus, Grafana, Datadog, or equivalent

Any experience with graph databases (Neo4j, TigerGraph, Amazon Neptune)

Interest in or exposure to fraud, risk, identity, fintech, or other high-stakes real-time domains

Exposure to ML workflows: feature pipelines, training data preparation, or model serving

What We Look For

You debug rather than guess, and you can explain what actually went wrong

You ask what the data is for before deciding how to model it

You're curious about the layer below the one you work in — how your code actually runs, where it fails, what it costs

You're comfortable being new to a tool and getting productive in it quickly

You care that numbers are right, because at Bureau a wrong number is a wrong risk decision

Our Culture

We hire self-motivated people and get out of their way

We value performance, not hours worked

Speed, ownership, and impact matter most

Compensation

Competitive salary + potential equity

Health benefits, flexible PTO, learning budget