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Tier-1 brand, mid-level data engineer in metro with broad tech stack increases competition.
Core data-engineering skills transfer across industries but banking compliance and Ab Initio specialization increase specificity.
Multiple mandatory years and specific required technologies create strict shortlisting.
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Lead moderately complex data engineering initiatives aligned with enterprise strategy deliverables.
Build and maintain optimized, highly available data pipelines supporting analysis and reporting.
Oversee data integration including data modeling, warehouse maintenance, scripting, and compliance adherence.
Minimum 4 years of Data Engineering experience or equivalent (work, training, education).
Experience with Spark, Abinitio, Python, SQL, and data warehouse technologies (minimum 3 years each).
Experience with Hadoop, Hive, Kubernetes, Docker, Google Big Query, and Autosys (minimum 1-2 years).
Experience working in Agile environments using Scrum or Kanban.
Experienced in end-to-end data engineering project execution including code reviews focused on testability and code coverage.
Able to lead and collaborate with teams to resolve data engineering challenges and deliver against strategic goals.
Familiar with real-time data streaming (Kafka), non-relational databases (MongoDB/Neo4j), and cloud platforms such as Google Cloud Platform; GCP Associate Cloud Engineer certification is a plus.