Data Engineer

Must Have:

• 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.
  

Responsibilities:

• 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.
  

Qualifications:

• 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.