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Tier-1 brand, mid-level generalist data role, metro location, and broad skill requirements.
Data engineering skills are transferable across industries, though enterprise banking governance raises some domain specificity.
Explicit 4+ years requirement plus many specific enterprise data tools and compliance expectations.
Lead and deliver moderately complex technical initiatives and projects within data engineering and software domains.
Design, develop, test, debug, and document enterprise-scale data platforms and modernization efforts, including migration from legacy tech stacks.
Provide technical guidance, perform code reviews, and collaborate across teams in an Agile environment to ensure high-quality, scalable, and reliable data engineering solutions.
4+ years of software engineering or equivalent experience (work experience, training, military, education).
Hands-on experience with Python, Spark, Iceberg, Hive, Dremio or similar virtualization/semantic layer tools.
Proven strong skills in SQL development, tuning, and supporting large enterprise data environments with data lakes, warehousing, or lakehouse architectures.
Experience with cloud platforms (Azure or GCP), ETL tools (Ab Initio preferred), orchestration tools (Airflow preferred), and databases such as Oracle, MS SQL, or Teradata.
Experienced in designing and implementing modern data platforms with standardization, automation (CI/CD, DevOps), and cloud-native/open table formats (e.g., Iceberg).
Capability in AI and ML adoption within data engineering, including GenAI, Agentic AI, LLMs for RAG architectures, embedding pipelines, and automation of data tasks.
Skilled at working in fast-paced Agile settings with cross-functional collaboration, driving modernization and technically complex projects with attention to system performance, reliability, and security.