AWS Senior Data Engineer
Summary
The Senior Data Engineer will lead the modernization of legacy Microsoft SQL Server data platforms to AWS-native services like Amazon Aurora, Glue, and Redshift. The role involves designing scalable ETL pipelines, optimizing data models, and implementing CI/CD processes for cloud-native data solutions.
We are seeking an experienced Senior Data Engineer to support the modernisation of a legacy Microsoft SQL Server data platform to AWS-native services. The role will focus on modernising databases, ETL pipelines, data warehousing, and reporting solutions, transitioning from MS SQL Server, SSIS, SSRS, and SSAS to Amazon Aurora PostgreSQL, AWS Glue, Amazon Redshift, AWS data capability/ SnowFlake. The successful candidate will design, build, and optimize scalable cloud-native data solutions while ensuring data quality, performance, and operational stability.
Key Responsibilities
- Modernization SQL Server databases to Amazon Aurora PostgreSQL.
- Analyze and convert SSIS ETL workflows into AWS Glue jobs and workflows.
- Migrate SSAS models and analytical workloads into Amazon Redshift.
- Rebuild SSRS reports and dashboards using AWS Quick.
- Design and develop scalable data ingestion, transformation, and reporting solutions.
- Develop and optimise data models, schemas, and database performance.
- Build and maintain CI/CD processes for database and data pipeline deployments.
- Implement data quality, monitoring, and operational support capabilities.
- Collaborate with architects, developers, business stakeholders, and reporting teams to deliver migration outcomes.
- Support testing, reconciliation, cutover, and post-production activities.
Required Skills & Experience
- 5+ years' experience in Database and Data Engineering.
- Strong expertise in SQL and database performance tuning.
- Experience with AWS data services including:
- AWS Glue
- Amazon Redshift
- AWS Quick
- AWS DMS and Schema Conversion Tool (desirable)
- Strong SQL and data modelling skills.
- Proficiency in Python and/or PySpark
- Experience building ETL/ELT pipelines and cloud-native data solutions.
- Familiarity with Git, CI/CD, DevOps, and Agile delivery practices.
- Understanding of data governance, security, and operational support.
Success Measures
- Successful modernization /migration of data assets from Microsoft technologies to AWS-native services – AWS aurora, Glue, Redshift
- Delivery of scalable, reliable, and cost-effective cloud data solutions.
- Improved automation, performance, and maintainability of data pipelines and reporting platforms.
- Adoption of modern analytics capabilities through Redshift, AWS data capabilities and Snowflake