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Tier-1 brand, mid-level data role, metro location, and broad tech requirements increase competition.
Core data engineering skills transfer across industries, but banking regulatory and governance needs raise specificity.
Explicit 5+ years and mandatory Java/Python, Airflow, dbt, and governance controls make filters strict.
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Lead design and delivery of scalable, reliable data platforms and pipelines powering analytics, search, and AI workflows.
Own end-to-end responsibility for building curated datasets with clear contracts, lineage, and measurable SLAs/SLOs, ensuring security, privacy, resiliency, and auditability.
Set technical standards and lead engineering rigor across Java and Python implementations, including batch/stream processing, microservices, pipelines, and APIs, while partnering with cross-functional teams to integrate AI/ML effectively.
5+ years of applied software engineering experience with formal training or certification in software engineering.
Strong hands-on experience with Java and Python in production for high-throughput data processing and pipeline development.
Advanced SQL skills with experience in data modeling, schema design, pipeline orchestration (e.g., Airflow), and transformation frameworks (e.g., dbt).
Experience working in regulated enterprise environments with security, governance, and audit controls.
Experienced lead Data Engineer with hands-on ownership of large-scale batch and streaming data pipelines and curated dataset delivery.
Skilled in building and operating production-grade data platforms with strong SDLC discipline, including CI/CD, testing, and operational support ownership.
Proficient in integrating AI-assisted development tools and responsible AI practices within engineering workflows, capable of cross-functional leadership involving product, platform, and compliance teams.