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Strong Tier-1 brand plus a common Data Engineer title increases applicant competition to moderate levels.
Specialized Databricks and cloud data engineering skills are moderately transferable across industries.
Many mandatory Databricks, Spark, cloud, governance skills and seniority create strict shortlisting filters.
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Lead and implement large-scale data engineering solutions using Databricks platform including notebooks, clusters, jobs, and Delta Lake.
Manage configuration of Unity Catalog and RBAC to maintain security and data governance compliance.
Develop and optimize data processing pipelines with Scala, Python, PySpark, and SQL on Azure and AWS cloud environments, incorporating CI/CD practices with Azure DevOps.
Expertise in Databricks platform (Notebooks, Clusters, Jobs, Delta Lake) and Unity Catalog with RBAC configuration.
Hands-on experience with Azure cloud services including Data Factory, Blob Storage, and Azure Databricks.
Proficient in programming and data processing using Scala, Python, PySpark, and SQL; experience with CI/CD using Azure DevOps.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced senior-level data engineer with demonstrated success in large-scale cloud data engineering projects involving Databricks on Azure and AWS.
Strong technical ownership in security, governance, and operational optimization of data pipelines using Apache Spark and Databricks.
Able to manage and deliver complex data solutions within hybrid work environments focusing on cloud-based CI/CD workflows and real-world coding challenges.