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Mid-level data engineer title plus common Databricks/PySpark/cloud skills increases applicant competition moderately.
Core data engineering skills (PySpark, Databricks, cloud) are broadly transferable across industries.
Explicit 5-8 years plus mandatory Databricks, PySpark, Python, SQL and cloud experience makes screening strict.
Design, develop, and maintain data pipelines and workflows primarily on Databricks platform using Python, SQL, and Apache Spark (PySpark).
Manage cloud data engineering activities leveraging AWS or Azure services including storage, ETL, and analytics pipelines.
Implement data modeling using star schema/dimensional modeling and ensure integration with BI tools, maintaining data governance and security standards.
5-8 years of professional experience in data engineering.
Hands-on experience with Databricks including Delta Lake, notebooks, and workflows.
Proficiency in Python and SQL programming along with Apache Spark (preferably PySpark).
Experience with either AWS (S3, Glue, EMR) or Azure (ADLS Gen2, Data Factory, Synapse) cloud services.
Experienced with cloud-based data pipelines in enterprise environments using Databricks and related orchestration tools (e.g., Airflow).
Familiarity with data governance and security practices relevant to sensitive data handling.
Working knowledge or certification progress on Databricks Certified Data Engineer Associate and exposure to streaming data technologies.