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Tier-1 employer, metro location, mid-level generalist data role with broad stack attracts high competition.
Core data engineering skills are widely transferable, though banking compliance familiarity slightly raises domain specificity.
Explicit 4+ years requirement plus many specific tools and cloud/ETL expectations increases filter strictness.
Lead moderately complex technical initiatives and projects within software engineering domain, including design, coding, testing, debugging, and documentation.
Collaborate and provide guidance to peers and junior staff, acting as an escalation point for technical and project challenges.
Contribute to planning and modernization of application landscape utilizing modern data platforms, cloud-native technologies, and implementing development best practices including peer code reviews.
4+ years of software engineering experience or equivalent through work, training, military service, or education.
Hands-on experience with Python, Spark, Iceberg, Hive, and at least one major cloud platform such as Azure or GCP.
Strong SQL development and tuning skills; experience with ETL tools (preferably Ab Initio) and databases like Oracle, MS SQL, Teradata.
Experience with orchestration tools such as Autosys or Airflow; familiarity with large-scale distributed systems, data warehousing, data lakes, and lakehouse architectures.
Experienced in supporting and optimizing enterprise-scale data environments and distributed systems, ensuring reliability, scalability, and performance.
Hands-on with modern data platform design and implementation including standardization, reusable components, DevOps/CI-CD best practices, and cloud-native/open table formats like Iceberg.
Practical exposure to AI-related pipelines such as GenAI, Agentic AI, LLM adoption, and integrating AI with data systems for enhanced data quality and automation.