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Bureau is a unified risk decisioning platform for compliance, fraud, and transaction risks, powered by a 1 billion+ identity knowledge graph. Over 150 banks, fintechs, retailers, and digital platforms use Bureau globally. The company has raised $50M+ from investors including Sorenson Capital and PayPal Ventures and is expanding rapidly across APAC, Americas, and Europe.
In this role, you will build and maintain batch and streaming data pipelines that ingest, clean, and transform data from device SDKs, internal services, partner APIs, and third-party data providers. You'll write and own backend services and RESTful APIs that serve data to internal teams and real-time decisioning paths. You'll develop Airflow DAGs powering reporting, compliance analytics, model training, and feature pipelines, and maintain their health day-to-day. You'll write Spark jobs and SQL transformations against the data lake, tuning performance as needed. You'll add monitoring, alerting, and data quality checks to surface problems proactively. You'll debug production issues across the stack—from Kafka consumers to schema changes to query performance degradation. You'll work with the infrastructure your pipelines run on (containers, deployments, cluster configs) and help keep it stable and cost-effective. You'll contribute to identity graph work, modeling relationships between entities to surface fraud rings and hidden linkages. You'll write documentation and tests, participate in design reviews, and help maintain schemas and contracts as the system scales.
The culture emphasizes self-motivation, performance over hours, and speed, ownership, and impact. You'll be expected to debug rather than guess, ask what data is for before modeling it, and be curious about the layers below your work—how code runs, where it fails, what it costs.
REQUIREMENTS
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; ability to write production-quality, tested code
- Strong SQL: joins, window functions, aggregations, and understanding of query execution
- Working understanding of databases, including OLTP vs. OLAP systems
- Hands-on experience with at least one distributed data processing framework (Spark preferred) or 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 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
- Experience running or tuning distributed workloads: cluster sizing, resource tuning, bottleneck analysis
- Exposure to Kafka or MSK, or any streaming/event-driven system
- Experience with Airflow or similar orchestration tool
- Exposure to EMR, Athena, ClickHouse, Databricks, or Snowflake
- Awareness of lakehouse table formats (Iceberg, Delta Lake, Hudi)
- Familiarity with observability tooling: Prometheus, Grafana, Datadog, or equivalent
- Experience with graph databases (Neo4j, TigerGraph, Amazon Neptune)
- Interest in or exposure to fraud, risk, identity, fintech, or high-stakes real-time domains
- Exposure to ML workflows: feature pipelines, training data preparation, or model serving
