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Tier-1 employer, mid-level data engineer role with broad stack and metro context increases competition.
Specialized big-data, lakehouse tooling and financial controls require domain-specific experience, limiting transferability.
Explicit 4+ years, mandatory Spark/Python/cloud/lakehouse skills and financial compliance increase shortlisting strictness.
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Lead and deliver moderately complex data engineering initiatives and projects including design, coding, testing, debugging, and documentation.
Develop, optimize, and maintain large-scale data pipelines, including ELT/ETL, batch, and streaming workloads using Spark, Python, SQL, and lakehouse technologies.
Provide technical leadership and guidance to team members, act as escalation point, and collaborate with cross-functional teams to resolve challenges and align with enterprise standards.
4+ years of software engineering experience or equivalent (work experience, training, military, education).
Proficiency in Python, SQL, bash scripting with experience in big data technologies like Apache Spark, Hive, Hadoop.
Experience with orchestration tools such as Autosys or Airflow, and knowledge of REST APIs and CI/CD pipelines.
Work Experience Required: 4+ years in software/data engineering roles.
Strong hands-on experience building and optimizing large-scale structured and unstructured data pipelines within distributed systems and lakehouse architectures.
Proven ability to implement data quality frameworks, governance, and compliance controls relevant to financial data environments.
Experience working in cloud-native, containerized environments with automated testing, orchestration, and integration in Agile, multi-disciplinary teams.