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Remote role, common mid-level Data Engineer title, and 3–5 year band increase applicant competition.
Core Databricks, Spark, and cloud data-engineering skills are transferable, though lakehouse governance adds domain specificity.
Explicit 3–5 years plus mandatory Databricks, Spark, SQL, and cloud platform requirements enforce strict filtering.
Build, maintain, and operate production data ingestion and transformation pipelines following established Bronze→Silver→Gold medallion architecture using Databricks, Apache Spark, and cloud services across AWS, Azure, and GCP.
Implement and maintain Delta Lake storage, data governance, security controls, and automated data-quality checks as part of a multi-cloud enterprise data platform.
Collaborate closely with Senior Data Architect and cross-functional teams to translate architecture designs into scalable, high-performance, and maintainable data platform solutions, including CI/CD and pipeline monitoring.
3–5 years of hands-on Data Engineering or ETL/cloud data platform experience.
Proficiency with Databricks, Apache Spark/PySpark, and SQL for structured data transformation.
Experience with at least one major cloud platform (Microsoft Azure preferred; AWS/GCP also valued).
Knowledge of data engineering fundamentals: data modeling, quality, schema evolution, validation, and basic data security practices (RBAC, encryption).
Mid-level Data Engineer with hands-on expertise in modern lakehouse architecture (Delta Lake, medallion architecture) and multi-cloud environments.
Experienced implementing automated data quality, metadata governance (Unity Catalog, Microsoft Purview), and orchestration with tools like Azure Data Factory or Databricks Workflows.
Skilled in operationalizing data platforms via CI/CD pipelines, performance tuning, troubleshooting, and documentation in collaboration with architecture and product teams.