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Global brand, metro location, and generalist Data Engineer title increase applicant density despite specialized GCP/Data Mesh needs.
Core data engineering skills are transferable, though GCP/Data Mesh specialization narrows cross-industry fit somewhat.
Mandatory 6+ years plus many specific GCP, data-mesh, and tooling requirements raise screening strictness.
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Design, build, and maintain high-performance cloud data platforms and data products on Google Cloud Platform (GCP), implementing Data Mesh principles and dimensional modeling for BI, analytics, and AI/ML.
Lead design and build of foundational data infrastructure supporting advanced AI, Machine Learning, and Generative AI initiatives, integrating MLOps pipelines and generative AI capabilities.
Act as Subject Matter Expert on data engineering and GCP services, collaborating cross-functionally to operationalize data warehouses, pipelines with data quality, lineage, and governance frameworks in a decentralized environment.
6+ years of data engineering and analytics application development experience.
Strong expertise in Google Cloud Platform services including BigQuery, Dataflow, Dataproc, Pub/Sub, and Dataplex.
5+ years of SQL development experience and 2+ years professional experience in Java or Python and Apache Beam.
Bachelor’s degree in Computer Science or related scientific field.
Experienced with large-scale data architectures including Data Mesh and cloud-native data warehousing using dimensional modeling (Star Schema, Snowflake Schema).
Proven ability to design and productionalize data pipelines and infrastructure supporting advanced AI/ML workflows including MLOps and Generative AI.
Demonstrates strong technical leadership and collaboration skills in agile environments partnering with data science, product, and engineering teams.