Data Engineer - 1668

• Develop and maintain ETL/ELT pipelines using Databricks, PySpark, and SQL for marketing acquisition reporting.
• Migrate existing Snowflake SQL scripts and transformations into Databricks Unity Catalog.
• Build ingestion pipelines for external marketing vendor data.
• Perform data parsing, normalization, transformation, and validation.
• Implement data quality checks, monitoring alerts, and issue resolution workflows.
• Troubleshoot pipeline failures and improve processing performance.
• Support SLA monitoring, logging improvements, and pipeline reliability.
• Collaborate with analytics teams to ensure accurate reporting datasets.
• Participate in code reviews, documentation, and engineering best practices.

• 4+ years of Data Engineering experience.
• Strong SQL skills, including complex joins, window functions, and analytical queries.
• Hands-on production experience with PySpark and/or Spark SQL.
• Databricks experience, including Delta Lake and Unity Catalog.
• Strong understanding of ETL/ELT pipelines, data modeling, and distributed processing., * Minimum 4 years of professional Data Engineering experience or equivalent.
• Experience building production pipelines in Databricks.
• Strong understanding of Delta Lake, Unity Catalog, Lakehouse architecture, and data governance.
• Experience with AWS services such as S3, IAM, EC2, Glue, or Lambda.
• Experience with Apache Airflow or similar orchestration tools.
• Experience processing JSON, Parquet, CSV, and other semi-structured data.
• Experience with Git/GitHub, CI/CD, and modern DevOps practices.

Nice to Have:

• Experience migrating SQL transformations from Snowflake to Databricks.
• Experience working with marketing acquisition or advertising data.
• Experience implementing data quality monitoring and pipeline reliability improvements.