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Tier-1 brand, metro location, common mid-level data engineer title and 4+ years drive high applicant competition.
Cloud and Databricks skills are broadly transferable, though enterprise data governance increases domain specificity.
Explicit 4+ years and mandatory Azure/Databricks/PySpark skills create strict technical filters.
Lead design and development of cloud-based data and analytics platforms, managing ELT/ETL jobs, data transformation, orchestration, and visualization for large datasets.
Develop and maintain data pipelines, data models, and data warehouses using Azure and Databricks environments to ensure data quality and efficient data storage.
Collaborate with product managers to deliver superior product outcomes that drive business transformation and value.
4+ years of work experience in data engineering and cloud platforms.
Strong proficiency with Azure services (Azure Data Factory, Databricks, Azure SQL Database, Azure Data Lake Storage, Azure Synapse Analytics).
Experience with ETL tools/frameworks like Apache Spark, Databricks Delta, and programming languages such as PySpark, Python, SQL, or Scala.
Bachelor's or master's degree in computer science, data engineering, or related field.
Experienced in leading complex data engineering projects on cloud platforms, especially Azure and Databricks, with hands-on technical expertise.
Skilled in end-to-end data pipeline development, data modeling, governance, and performance optimization in enterprise environments.
Able to work closely with product teams to translate business needs into scalable, robust data solutions with measurable business impact.