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Strong brand, popular Data Engineer title, and broad multi-cloud lakehouse skillset increase competition.
Requires specialised lakehouse, BigQuery/Databricks, PySpark and multi-cloud expertise, limiting cross-industry transferability.
Explicit 8+ years and mandatory lakehouse, PySpark, BigQuery/Databricks, and multi-cloud skills enforce strict filters.
Design, build, and optimize scalable data lakehouse platforms using Google BigQuery or Databricks.
Develop, schedule, and maintain robust ETL/ELT data pipelines with native ingestion tools and multi-task workflows.
Ensure data quality, governance, security, and performance optimization across multi-cloud environments (Azure, AWS, GCP).
Bachelor’s degree in Computer Science, Engineering, Mathematics, or related technical field preferred.
8+ years of experience in data engineering, focusing on distributed system data pipelines.
Deep hands-on expertise in Google BigQuery and/or Databricks lakehouse platforms, SQL, Python, and Apache Spark (PySpark).
Strong experience with cloud platforms Azure, AWS, and GCP, including storage, security, and networking; experience with data governance tools like Dataplex or Unity Catalog.
Experienced in designing and implementing Medallion Architecture (Bronze, Silver, Gold) in lakehouse environments.
Skilled in performance tuning of BigQuery and Spark jobs, with expertise in cost optimization and workload diagnostics.
Able to manage complex data workflows and collaborate effectively with cross-functional technical and non-technical teams in multi-cloud data ecosystems.