Back
Metro location, common Senior Data Engineer title, mid-level range, and broad cloud/streaming requirements.
Big-data engineering skills are broadly transferable, though fraud/identity specialization raises domain sensitivity.
Explicit 4–8 year requirement plus many mandatory big-data, cloud, and orchestration skills.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and own the cloud data lake and lakehouse architecture serving identity signals and fraud detection products.
Build and operate scalable batch and streaming data pipelines ingesting from diverse sources, ensuring real-time decisioning capabilities.
Maintain production data infrastructure reliability, monitoring, cost-efficiency, and collaborate with data science to optimize feature pipelines for ML models.
4-8 years of hands-on big data engineering experience with batch and streaming on cloud (preferably AWS).
Proven expertise with data lake technologies: EMR, Spark, S3, Athena and warehouses/lakehouses like ClickHouse, Databricks, or Snowflake.
Experience with pipeline orchestration tools (Airflow or Astronomer) and programming in Python and/or Scala/Java plus expert SQL skills.
Familiarity with AWS ecosystem, Kubernetes, MSK/Kafka, RDS, and building systems supporting both batch and real-time APIs.
Experienced in building secure, real-time data platforms with large-scale streaming and batch workflows at cloud scale.
Strong understanding of OLAP vs OLTP systems for optimal data architecture decisions aligned with fraud/risk use cases.
Comfortable designing and operating data infrastructure with SLAs and monitoring in a high-stakes, fast-moving fintech or fraud detection environment.