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Tier-1 brand, mid-level generalist role, metro location, and broad skills increase candidate competition.
Core data engineering skills are transferable, but banking compliance and enterprise-scale experience increase domain sensitivity.
Explicit 4+ years requirement plus mandatory data engineering, cloud, and ETL stack makes shortlisting strict.
Lead and deliver moderately complex technical initiatives within software engineering and data domains.
Design, code, test, debug, and document software projects related to data engineering and enterprise data environments.
Provide technical leadership and guidance to peers and less experienced staff, including resolving technical issues and overseeing code quality.
4+ years of software engineering experience or equivalent through work experience, training, military experience, or education.
Proficiency with SQL development and tuning, and experience supporting large enterprise-scale data environments.
Experience with at least some of these technologies: Python, Spark, Iceberg, Hive, Dremio or similar, any major cloud platform (Azure or GCP), ETL tools (Ab Initio preferred, Informatica, DataStage), databases (Oracle, MS SQL, Teradata), orchestration tools (Autosys, Airflow preferred).
Ability to work in an Agile, fast-paced environment; strong communication skills with cross-technical and functional teams.
Experienced in designing and implementing modern data platforms leveraging data warehousing, data lakes, and lakehouse architectures with emphasis on scalability and performance.
Familiar with modern development best practices including CI/CD, DevOps, automated code generation, cloud-native technologies, and open-table formats like Iceberg.
Knowledgeable in emerging AI technologies relevant to data engineering such as GenAI, Agentic AI, LLM adoption, vector stores, and integration of AI platforms for tasks like data quality and metadata extraction.