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Strong global brand, metro location, popular Data Engineer role and broad skillset drive high competition.
Requires specialized streaming engagement and media analytics experience, reducing but not eliminating cross-industry transferability.
Explicit 10+ years plus mandatory Scala/Spark/Databricks and large-scale data platform experience tightens shortlisting.
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Own the end-to-end architecture of the multi-tenant product engagement data platform processing over 30 billion daily events, including streaming ingestion, sessionization, and attributed analytics layers.
Set technical direction for high-performance Spark applications and data modeling conventions, and lead engineering standards across shared libraries and tenant-specific pipelines.
Drive engineering excellence in data quality, observability, and scalability at petabyte scale, mentor senior engineers, and collaborate with analytics, data science, and platform teams to meet business-critical engagement metrics requirements.
10+ years experience in data engineering or related software engineering with technical leadership on large-scale data platforms.
Expert proficiency in Scala (strongly preferred) and/or Python, with strong software engineering practices including CI/CD.
Deep expertise in Apache Spark (streaming and batch) and experience with Delta Lake/lakehouse architectures on Databricks and AWS (S3).
Advanced SQL skills for large-scale transformations and validation; Experience with versioned artifact releases and CI/CD with GitHub Actions.
Experienced in building and scaling high-traffic, event-based analytics platforms, especially in streaming media or consumer applications.
Proven architectural decision-maker with hands-on leadership in multi-tenant Spark and Scala data platforms including sessionization, user-journey modeling, and attribution.
Familiar with data quality frameworks, orchestration tools like Databricks Workflows/Airflow, semantic layers (Looker/LookML), and infrastructure-as-code practices.