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Metro location, popular data engineer title, and broad required skillset drive high competition.
Core data engineering skills are transferable, though regulated enterprise experience moderately biases fit.
Explicit 8-10 years plus mandatory Databricks, Spark, AWS, and streaming requirements create high strictness.
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Design, build, and operate scalable, cloud-native data pipelines and platforms mainly on AWS, ensuring production readiness, security, observability, and cost efficiency.
Work with technologies including Databricks, PostgreSQL, Aurora, Spark, Airflow, and Kafka for ETL/ELT processes in enterprise and regulated client environments.
Collaborate cross-functionally with analysts, data scientists, and platform teams while mentoring junior engineers and improving standards and system performance.
8-10 years of hands-on data engineering experience.
Strong skills with Databricks, Python, Java, Spark, advanced SQL, and operating data platforms at scale.
Experience with AWS data services and cloud-native platforms, building and running production batch and streaming data pipelines.
Experience with distributed systems, data modeling, data quality, CI/CD, monitoring, and operational support of data platforms.
Experienced engineer able to take full ownership of complex data solution delivery in production at scale within enterprise or regulated environments.
Demonstrated ability to lead architectural and data modeling decisions and improve system scalability, reliability, and performance.
Capable of mentoring junior staff and collaborating across analytics, AI, and platform teams in client-facing or consulting-style roles.