Data Engineer

Bureau

Responsibilities

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

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

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

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

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

Health benefits, flexible PTO, learning budget