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Tier-1 employer, metro location, popular mid-level data-engineer title, and 5+ years requirement drive high competition.
Core data engineering skills are transferable, though regulated-banking controls slightly reduce cross-industry fit.
Many mandatory technical skills, 5+ years experience, lead responsibility, and regulated environment enforce strict filters.
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Lead design, delivery, and operation of reliable, scalable batch and streaming data platforms and pipelines powering critical business use cases.
Own data modeling, curation, and ETL/ELT workflows with strong validation, SLAs/SLOs, lineage, and compliance controls.
Set engineering standards and lead adoption of AI-assisted development tools while partnering across teams for governance and operational stability.
5+ years of applied software engineering experience with formal training or certification.
Hands-on proficiency in Java and Python for production-grade data platforms, including strong SQL and data modeling skills.
Experience with pipeline orchestration tools (e.g., Airflow), transformation frameworks (e.g., dbt), and maintenance of secure, scalable data processing solutions.
Experience building and operating search/indexing workflows (e.g., Elasticsearch) and production support ownership in a regulated environment.
Experienced lead Data Engineer skilled in end-to-end building and operation of curated datasets and production data pipelines in enterprise settings.
Proficient in secure engineering practices, cross-functional collaboration, and advancing team usage of AI-assisted development workflows.
Familiar with cloud-native environments (AWS), large-scale distributed processing (Spark), event streaming architectures, and regulated risk and control frameworks.