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Metro senior data role with common ETL demand but Databricks specialization gives moderate applicant competition.
Core PySpark/Databricks ETL skills are highly transferable across industries.
Mandatory Databricks, PySpark, Delta Lake and streaming stack enforces strict technical shortlisting.
Design and implement scalable batch and streaming data pipelines using PySpark, Databricks, and related distributed processing frameworks.
Architect and optimize data ingestion, transformation, and processing solutions on platforms like Snowflake and Delta Lake to support enterprise-scale data.
Lead data governance, quality assurance, workflow orchestration (Apache Airflow or Databricks Workflows), and operational monitoring for reliable data platform delivery.
Proficiency with PySpark, Databricks Workflows, Delta Lake on Databricks, and Amazon Kinesis.
Experience with data engineering on cloud platforms, specifically AWS services including AWS SNS, SQS, Kinesis, Glue, EMR, Redshift, and associated security/monitoring tools.
Strong skills in SQL, Python, and Unix/Linux shell scripting relevant to ETL and data integration tasks.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in designing end-to-end data engineering solutions aligned with business analytical objectives and modern Lakehouse architecture principles.
Comfortable driving data quality, validation, and governance in complex, distributed data ecosystems with high operational visibility demands.
Skilled in leveraging AI-assisted engineering techniques to enhance productivity, testing, and documentation within data engineering workflows.