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Metro mid-level data engineer title and experience band increase competition despite Databricks specialization.
Databricks and Lakehouse focus makes background transferability limited across industries.
Strong mandatory Databricks and PySpark requirement increases screening despite no years specified.
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Design, develop, and maintain ELT pipelines using Databricks-native orchestration and related technologies.
Build and optimize large-scale data processing workflows with PySpark and SQL, including Delta Live Tables with data quality enforcement.
Implement and support data Lakehouse architectures and data warehousing solutions while ensuring data quality and governance.
Strong hands-on experience with Databricks including Delta Lake, Workflows, Jobs, and Delta Live Tables.
Proficient in PySpark and SQL with proven experience building and supporting ELT pipelines at scale.
Experience with data modeling and understanding of data Lakehouse and warehousing concepts.
Work Experience Required: Not explicitly mentioned in the JD.
Expertise in Databricks-native orchestration (Workflows, Jobs) as primary pipeline orchestration mechanism.
Experience with cloud platforms, preferably Azure, and familiarity with version control tools like Git.
Exposure to enterprise consulting or regulated environments and working with globally distributed teams.