Senior Data Engineer

Overview

Senior Data Engineer responsible for designing and owning Bureau’s data platform for fraud detection and identity intelligence.

What you'll do

• Design and architect a cloud data lake and lakehouse as a single source of truth.
• Build and operate scalable batch and streaming data pipelines for ingestion, cleaning, transformation, and aggregation.
• Develop orchestration workflows in Airflow for research, reporting, compliance analytics, and ML training.
• Own reliability, observability, and cost-efficiency with monitoring, alerting, and SLAs for real-time risk decisions.
• Collaborate with data science and ML teams on feature stores and data pipelines.
• Drive data engineering best practices including data quality, schema governance, and testing.
• Contribute to graph-based fraud intelligence using graph databases.

What you'll need

• 4-8 years of hands-on big data engineering experience (batch and streaming) on the cloud.
• Deep data lake stack experience including EMR, Spark, S3, Athena, and lakehouses/warehouses like ClickHouse, Databricks, or Snowflake.
• Strong knowledge of OLAP and OLTP and when to use each.
• Production experience with Airflow (or Astronomer).
• Familiarity with AWS and Kubernetes ecosystem including EMR on EKS/self-hosted K8s, MSK/Kafka, and RDS.
• Experience building RESTful APIs enabling batch and real-time workloads with monitoring and instrumentation.
• Strong programming skills in Python and/or Scala/Java, plus expert-level SQL.

Nice to have

• Graph database experience with Neo4j, TigerGraph, or Amazon Neptune.
• Exposure to fraud detection, risk, identity, fintech, or other high-stakes real-time data domains.
• Experience with data quality frameworks, data cataloging, or lakehouse table formats such as Iceberg, Delta Lake, or Hudi.
• Experience supporting ML platforms including feature stores, training pipelines, or model-serving data flows.

Details

• Role focuses on high-throughput streaming signals and low-latency serving for fraud detection.