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Tier-1 brand and metro location but senior, specialized technical profile reduces applicant density.
Specialized Spark/Scala and Databricks skills transfer, but require large-scale event engineering experience.
Mandatory 10+ years and specialized Spark/Scala/Databricks expertise make filters strict.
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Own end-to-end architecture of multi-tenant product engagement data platform processing 30+ billion events daily, including streaming ingestion, sessionization, attribution, and component performance layers.
Set technical direction and standards for high-performance Scala and Spark data pipelines on Databricks and AWS, focusing on scalability, cost-efficiency, and reliability.
Drive engineering excellence via mentorship, cross-team initiatives, data quality frameworks, and roadmap planning for engagement data platform.
10+ years experience in data engineering or related software engineering with technical leadership on large-scale data platforms.
Expert-level proficiency in Scala (strongly preferred) and/or Python; strong software engineering skills including testing, modularity, CI/CD.
Deep expertise with Apache Spark (structured streaming and batch), performance tuning, skew/shuffle optimization at petabyte scale.
Hands-on experience with Databricks, AWS (S3), Delta Lake, SQL, and CI/CD tools (e.g., GitHub Actions).
Experienced in building large-scale event-based analytics platforms including sessionization, pathing, and user-journey attribution.
Strong architectural ownership abilities with a proven record of mentoring engineers and driving best engineering practices.
Familiarity with multi-tenant data platforms, semantic layers, orchestration tools (Databricks Workflows, Airflow), data quality and governance frameworks.