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Tier-1 brand, metro location and common data role increase competition, despite seniority and GCP specialization.
GCP and Spark focus moderately restrict cross-industry moves, though data engineering skills remain transferable.
Explicit 8-15 years and mandatory GCP, Spark, Python, Dataproc and FinOps requirements make filters strict.
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Lead design and delivery of GCP cloud-based data platforms using Cloud Run, Cloud SQL, Pub/Sub, GCS, BigQuery, and orchestration with Cloud Composer/Airflow.
Drive performance engineering for large-scale Spark and Dataproc workloads, including workload testing, failure troubleshooting, and cost-performance benchmarking.
Provide technical leadership and architecture guidance to engineering teams while managing FinOps, capacity planning, and cost governance for data platforms.
8 to 15 years of data engineering experience with strong GCP expertise.
Proficiency with Python, Spark, Dataproc, GCS, BigQuery, Cloud Run, Cloud Functions, Cloud SQL PostgreSQL, and Pub/Sub.
Experience administering, optimizing, and managing Cloud Composer/Airflow including FinOps.
Demonstrated ability in Spark UI debugging, YARN diagnostics, workload benchmarking, and managing data platform performance on GCP.
Experienced leader capable of overseeing both strategic platform delivery and hands-on performance optimization in a cloud data environment.
Strong technical depth in GCP data services combined with proficiency in performance engineering of big data workloads.
Comfortable bridging technical architecture with stakeholder management and team leadership within complex data modernization projects.