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Tier-1 brand, metro location, mid-level generalist data-engineer with common tech stack increases competition.
Core data engineering skills transfer well, but banking domain and enterprise tools require some domain knowledge.
Mandatory 3+ years plus Databricks/Spark/Python/SQL and SDLC requirements make filtering strict.
Develop and maintain scalable ELT data pipelines and data architectures using Python, Spark/PySpark, Databricks, and SQL.
Implement data security, governance, and entitlements frameworks to ensure enterprise data protection.
Use SDLC practices including CI/CD, testing, and operational monitoring to ensure pipeline stability and delivery of production-ready solutions.
3+ years of applied software engineering experience with formal training or certification.
Hands-on experience with Databricks, Spark/PySpark, Python, and SQL required.
Familiarity with cloud platforms (AWS) and distributed data processing.
Experience using enterprise-authorized AI-assisted software development tools with demonstrated ability to validate AI-generated outputs.
Experienced in building data pipelines supporting enterprise analytics and data lifecycle management.
Demonstrated ability to apply best practices in data engineering for performance, reliability, and maintainability within agile teams.
Knowledgeable about responsible AI use in engineering workflows, including data sensitivity and secure handling, capable of guiding peers in safe AI tool usage.