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Senior, niche Databricks skills but metro location yields moderate candidate density.
Data engineering skills are transferable across industries, but enterprise/regulatory experience increases domain specificity.
Mandatory 10+ years and specific Databricks, Spark, AWS, Airflow, Kafka skills enforce strict filtering.
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Design, build, and operate scalable, cloud-native data pipelines and platforms primarily on AWS and Databricks for enterprise and regulated clients.
Ensure production readiness of data solutions focusing on security, testing, observability, and cost-efficiency while optimizing performance and data quality.
Collaborate with cross-functional teams including analysts, data scientists, and AI engineers; mentor junior engineers and contribute to shared technical standards.
10+ years of hands-on data engineering experience.
Strong expertise with Databricks, Python, Java, Spark, and advanced SQL.
Proven experience designing data platforms and running production-scale data pipelines on cloud-native AWS environments.
Experience with distributed systems, data modeling, batch and streaming pipelines, and operational support like CI/CD and monitoring.
Experienced engineer comfortable making architectural and data modeling decisions in complex, enterprise-scale environments.
Delivery-focused with a track record working on regulated or security-conscious industries or clients.
Able to lead technical discussions, improve system scalability and reliability, and mentor junior engineers in best practices.