Job Summary
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:
- Amazon Aurora PostgreSQL
- 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.
Key Responsibilities
Key Responsibilities
- Modernisation SQL Server databases to Amazon Aurora PostgreSQL.
- Analyse 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.
Skill Requirements
Language - Python, SQL
Data Technology - AWS Glue, Redshift, Athena, Iceberg, Flink, Kafka(understanding of Big Data Tech)
Cloud/Hosting - AWS, Understanding of Network design, AWS native services such as step functions
Data Pipelines - Airflow, DAta Ingestion, transformation, governance, semantic and physical Data Modeling
APIs & Microservices - RESTful services, automation scripts
DevOps - CI/CD (Jenkins, Git), Docker, Kubernetes, IAM Vault and Operations Mindset
Security & Compliance - Data goverance, risk management
Agile Delivery - Colaboration with squads and stakeholders