Data Engineering - Aws

Data Engineer responsible for designing, developing, and optimizing ETL/ELT pipelines and data warehouse solutions using SQL, cloud data platforms, and modern data engineering tools.

• Design, develop, and optimize ETL/ELT pipelines to ingest data from multiple sources into the data warehouse
• Build and maintain dimensional data models (star/snowflake schemas), fact and dimension tables, and slowly changing dimensions
• Write complex, performant SQL including joins, window functions, CTEs, and stored procedures and tune queries for large datasets
• Ensure data quality, integrity, and consistency through validation, reconciliation, and automated checks
• Optimize warehouse performance via indexing, partitioning, and query tuning
• Maintain documentation of data flows, models, and transformation logic
• Monitor, troubleshoot, and resolve pipeline failures and data discrepancies

• Strong SQL advanced query writing, optimization, and stored procedures
• Solid data warehousing fundamentals: dimensional modeling, ETL/ELT design, and schema design
• Experience with relational database/warehouse platforms (SQL Server, PostgreSQL, Snowflake, Redshift, or BigQuery)
• Understanding of ETL tools or orchestration frameworks (Azure Data Factory, SSIS, Airflow, or dbt)
• Strong grasp of data quality, performance tuning, and troubleshooting
• Databricks experience with Delta Lake, Workflows, and the Medallion architecture
• PySpark building and optimizing distributed data processing jobs
• Experience with cloud data platforms (Azure, AWS, or GCP)
• Familiarity with BI tools (Power BI, Tableau)
• Python or scripting for automation
• Version control (Git) and CI/CD for data pipelines