JOB REQUIREMENTS:

• Tertiary education (Degree or Diploma) in a relevant field is preferred.
• At least 5 years of working experience in systems analysis, systems design, systems development and systems quality assurance.
• Strong Database and SQL knowledge for writing complex queries and performance tuning;
• Extensive experience in Data Engineering and BI tools like IBM DataStage, Informatica, Cognos Analytics, Power BI;
• Strong understanding of OLAP, ETL/ELT and BI design concepts and best practices;
• Exposure in Cloud Data platforms like Snowflake, Databricks, AWS Redshift will be an advantage.

For support of the Statistical Systems, the candidate shall have relevant working experience in the following platforms and programming languages:

• IBM Mainframe MVS/ESA
• Python

• Design and develop scalable data pipelines: Build, optimize, and manage robust ETL (Extract, Transform, Load) processes that ingest and transform large data sets from multiple sources into a clean, structured format.
• Data modeling: Implement best practices for data modelling, schema design, and query optimization to support high-performance data storage and retrieval.
• Ensure data pipeline performance, integrity, and security by monitoring system health, diagnosing issues, and implementing improvements.
• Implement data governance policies: Enforce data governance and security policies to ensure data integrity, privacy, and compliance with regulatory standards.
• Automation: Automate repetitive tasks and implement proactive monitoring solutions to ensure high data quality and minimize manual intervention.
• Gather requirements from users, designing, implementing, and maintaining application systems according to the project needs and goals.
• Developed Databricks notebooks for data transformation/cleansing.
• Troubleshooted data issue encountered in the Databricks and determine the code fix which resulted to the alignment of data between Databricks and SAS in Production environment.
• Provide continuous support for the Statistical Systems and Data Warehouse.