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Tier-1 brand, mid-level generalist title, metro location, and broad applicant pool.
Core data engineering skills are transferable but financial risk/treasury and compliance needs increase domain sensitivity.
Multiple mandatory technologies, cloud/data governance requirements, and explicit 4+ years experience.
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Design, develop, optimize, and maintain scalable data engineering frameworks supporting critical financial risk processes.
Lead moderately complex data engineering initiatives including Data Center exit migrations, DPC onboarding, and modernization projects.
Build and maintain resilient data pipelines, APIs, and automation for cloud and on-prem data ecosystems with strong data quality and performance focus.
4+ years of Software Engineering experience or equivalent.
Hands-on experience with Python, SQL, Apache Spark, Hive, Hadoop, and orchestration tools like Autosys or Airflow.
Experience with cloud platforms, REST APIs, containerization (Docker, Kubernetes), and CI/CD workflows.
Work Experience Required: 4+ years
Proven experience leading moderately complex data engineering projects in financial services or similar regulated environments.
Strong expertise in building big data pipelines using distributed computing frameworks and modern lakehouse architectures (Iceberg, Delta, Medallion).
Demonstrated ability to design automated data quality frameworks and implement secure, scalable cloud-native data solutions.