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Tier-1 brand, metro location, mid-level Data Engineer title, and broad GCP/Spark skillset create high applicant competition.
Core data engineering skills (Python, Spark, GCP, BigQuery) are broadly transferable across industries.
Explicit 4+ years plus extensive mandatory GCP/Spark/Python stack and AI skills make filters moderately strict.
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Lead design and build of scalable data pipelines and platforms on GCP to support AI and GenAI data solutions.
Oversee data integration, data modeling, data warehouse maintenance, and scripting for analysis to facilitate enterprise data strategy.
Resolve complex data engineering issues and lead initiatives involving batch and streaming solutions using technologies like Python, Spark, BigQuery, and Kafka.
4+ years of data engineering experience or equivalent demonstrated through work experience, training, military, or education.
Strong hands-on experience with Python, SQL, Spark, and GCP services including BigQuery, Dataproc, Composer, and Pub/Sub.
Experience with large-scale ETL/ELT and streaming data pipeline development and deployment.
Understanding of data modeling, governance, cloud architecture, and compliance requirements.
Proven ability to lead moderately complex technical initiatives and collaborate with cross-functional teams to meet strategic data goals.
Experience building AI-enabled data products supporting semantic search, RAG, embeddings, knowledge graphs, and integrating APIs for AI workflows.
Comfortable implementing engineering excellence via automation, CI/CD, monitoring, reusable frameworks, and familiarity with modern AI, ML Ops, and vector database concepts.