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Mid-level data engineer, metro location, broad skills and mid experience band increase competition.
Banking transaction, on-premise and PII/security requirements raise domain-specific fit sensitivity.
Explicit years, mandatory Spark/Python/Airflow, and banking security make filters strict.
Design, develop, and manage scalable, reliable production-grade data pipelines handling large volumes of banking and transaction data.
Implement robust data quality controls, reconciliation, automated testing, and governance across pipelines to ensure data accuracy and lineage.
Collaborate cross-functionally with ML, architecture, backend, and infrastructure teams to deliver secure, high-performance data solutions within restricted banking environments.
5-9 years of relevant experience in Data Engineering with ownership of production-grade data pipelines.
Strong hands-on expertise in Python, advanced SQL (including window functions, query optimization), and distributed data processing platforms like Apache Spark/PySpark.
Experience with pipeline orchestration tools such as Apache Airflow.
Experience or understanding of data modeling, ETL/ELT processes, CDC/incremental ingestion, and working knowledge of Git, CI/CD, Docker, and Linux environments.
Experienced in building and optimizing large-scale, secure data pipelines specifically in banking or similarly restricted data environments.
Familiar with implementing automated data quality frameworks and managing feature engineering pipelines supporting ML workflows.
Capable of designing for scalability, performance tuning, failure recovery, and cross-team collaboration involving ML, backend engineering, and infrastructure.