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Tier-1 brand, common mid-level data engineer title, metro location, and broad skillset requirements heighten competition.
Core data engineering skills are transferable but Abinitio and banking compliance needs increase domain specificity.
Multiple explicit years and mandatory tech requirements make shortlisting stringent.
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Lead moderately complex data engineering initiatives related to enterprise strategy and large-scale data processing frameworks.
Build and maintain optimized, highly available data pipelines and oversee data integration including data modeling and data warehouse management.
Resolve technical challenges, lead teams to meet deliverables, and ensure compliance with data policies and procedures.
Minimum 4+ years of data engineering experience (including work experience, training, military experience, or education).
3+ years of experience each in Spark, Abinitio, Python or other scripting language, data warehouse, and SQL.
1+ years of experience with Hadoop, Hive, Python, Kubernetes, Docker, and Google Big Query.
Experience working in Agile environments (Scrum or Kanban); ability to conduct code reviews focused on testability and code coverage.
Experience executing end-to-end data engineering projects and presenting technical content to both technical and executive audiences.
Familiarity with non-relational databases (e.g., MongoDB, Neo4j) and real-time data streaming tools like Kafka.
Knowledge of cloud ecosystems, especially Google Cloud Platform; certifications like GCP Associate Cloud Engineer considered a plus.