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Large consulting brand, popular mid-level data engineer title, metro location, and 3–5 year band increase applicant competition.
GCP-focused data engineering skills are transferable but require cloud-specific experience, yielding medium cross-industry fit.
Explicit 3–5 year requirement plus mandatory GCP, BigQuery, and Spark experience makes shortlisting stringent.
Design, build, and optimize complex ETL/ELT data pipelines on Google Cloud Platform focusing on scalability and performance.
Manage and tune large-scale Apache Spark jobs and optimize BigQuery data warehouse queries for high performance.
Bridge traditional data engineering with emerging GenAI capabilities by exploring integration of Vertex AI into production pipelines.
3-5 years of relevant experience in data engineering or software development.
Bachelor's degree in Computer Science or related field.
Proven expertise in Python or Scala programming and tuning/debugging Spark at scale.
Deep hands-on experience with Google Cloud data services: BigQuery, Dataflow, Dataproc, Pub/Sub.
Experienced in architecting and managing large-scale data infrastructure leveraging GCP native tools across batch and real-time pipelines.
Comfortable working with advanced SQL (window functions, CTEs) and building performant data models.
Open to collaborating with data science teams on operationalizing GenAI and related AI/ML integrations within data pipelines.