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Tier-1 brand, mid-level generalist data engineer role in metro markets creates high applicant competition.
Core data engineering skills are transferable, though banking compliance experience is beneficial.
Explicit 4+ years and many mandatory data stack skills creates high filter rigidity.
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Lead and deliver moderately complex data engineering initiatives aligned with enterprise strategy.
Build, maintain, and optimize highly available data pipelines and data warehouse environments for analysis and reporting.
Oversee data integration work, including data modeling, scripting, and compliance with data policies and procedures.
Minimum 4 years of Data Engineering experience.
Experience with Spark, Abinitio, Python (or other scripting), SQL, and data warehousing (all 3+ years preferred).
Experience with Hadoop, Hive, Kubernetes, Docker, Google Big Query, and Autosys (minimum 1-2 years each).
Work Experience Required: Minimum 4 years Data Engineering experience; Notice period: Not explicitly mentioned.
Experienced in delivering end-to-end data engineering projects in Agile environments using Scrum or Kanban.
Skilled in conducting code reviews focusing on testability and code coverage, and presenting to technical and executive audiences.
Familiar with cloud ecosystems (Google Cloud Platform), real-time data streaming (Kafka), and non-relational DBMS (MongoDB or Neo4j); GCP Associate Cloud Engineer certification is a plus.