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Tier-1 brand, metro location, mid-level seniority and broad common skillset increase applicant competition.
Core data engineering skills transfer across industries, though financial domain experience is beneficial.
Explicit years, leadership requirement and specific big-data tech stack make shortlisting strict and selective.
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Lead and mentor a team of entry to mid-level software engineers working on data engineering tasks including ETL transformations and big data platform development.
Design, oversee, and contribute to data architecture, dataflows, and transformation of ETL logic on AWS or Databricks platforms.
Set and scale AI-assisted engineering and SDLC automation practices across multiple teams to improve delivery speed, quality, and operational outcomes with measurable expectations.
5+ years of applied software engineering experience with 2+ years leading technologists in complex technical problem solving.
Advanced knowledge of application, data, and infrastructure architecture; strong experience with Big Data technologies (AWS, Spark, Kafka, DataBricks), Java, Python, and SQL ETL.
Strong UNIX shell scripting skills and familiarity with relational databases (Oracle, SQL Server).
Experience with data quality testing and strong analytical skills as well as experience leading multi-team adoption of AI-assisted development and delivery tools including governance and secure handling of sensitive data.
Experienced in managing and coaching software engineering teams focused on data engineering and big data platform solutions within complex environments.
Comfortable implementing and scaling AI-assisted software development lifecycle automation practices.
Knowledgeable about responsible AI use in engineering workflows, including security, resiliency, and control measures, and able to influence leadership on these topics.