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Tier-1 brand, broad data engineer title, and 4+ years mid-level requirement increase applicant competition.
Core data engineering skills are transferable, but Abinitio and banking compliance increase domain specificity.
Explicit 4+ years plus many mandatory tech stack items and certifications make shortlisting highly strict.
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Lead and deliver moderately complex data engineering initiatives aligned with enterprise strategy.
Build, optimize, and maintain highly available data pipelines and data integration frameworks including data models and data warehouses.
Resolve data engineering challenges ensuring compliance with data policies, and collaborate with teams to achieve strategic goals.
Minimum 4 years of Data Engineering experience (via work, training, or education).
Experience with Spark, Abinitio, Python, SQL, and data warehousing noted as important though specific thresholds specified as desired rather than mandatory.
Work experience with Hadoop, Hive, Python, Kubernetes, Docker, Google Big Query, and Autosys mentioned but not strictly mandatory.
Work Experience Required: Minimum 4 years of Data Engineering experience; other skills are desired but not explicitly listed as mandatory filters.
Experienced in executing end-to-end data engineering projects and working in Agile environments (Scrum or Kanban).
Comfortable conducting code reviews focused on testability and code coverage, and presenting technical content to both technical and executive audiences.
Familiarity with real-time data streaming (Kafka), non-relational databases (MongoDB, Neo4j), and cloud platforms such as Google Cloud Platform is an advantage.