Lead AWS Data Engineer

Senior AWS Data Engineer

• PySpark ramp-up
• Glue job hands-on proof
• Dimensional modeling

• Develop and maintain PySpark-based ETL pipelines for batch and incremental data processing
• Build and operate AWS Glue Spark jobs (batch and event-driven), including:
  • Job configuration, scaling, retries, and cost optimization
  • Glue Catalog and schema management
• Design and maintain event-driven data workflows triggered by S3, Event Bridge, or streaming sources
• Load and transform data into Amazon Redshift, optimizing for:
  • Distribution and sort keys
  • Incremental loads and upserts
  • Query performance and concurrency
• Design and implement dimensional data models (star/snowflake schemas), including:
  • Fact and dimension tables
  • Slowly Changing Dimensions (SCDs)
  • Grain definition and data quality controls
• Collaborate with analytics and reporting teams to ensure the warehouse is BI-ready
• Monitor, troubleshoot, and optimize data pipelines for reliability and performance

• Strong PySpark experience (Spark SQL, Data Frames, performance tuning)
• Hands-on experience with AWS Glue (Spark jobs, not just crawlers)
• Experience loading and optimizing data in Amazon Redshift
• Proven experience designing dimensional data warehouse schemas
• Familiarity with AWS-native data services (S3, IAM, Cloud Watch)
• Production ownership mindset (debugging, failures, reprocessing)

• SPARK SQL
• DATA SERVICES
• MODELING
• PYSPARK
• AWS