Design and build robust ETL pipelines to extract, transform, and load data from diverse sources into a data warehouse, ensuring data integrity and efficient processing. Develop and maintain complex SQL queries, stored procedures, and views to support data analysis, reporting, and business intelligence initiatives. Create and optimize data models, dimensional/star schema to facilitate efficient data retrieval and reporting performance within the BI environment.
Design and implement data governance and data quality processes to ensure data accuracy, consistency, and reliability across the organization. Develop and maintain interactive dashboards and reports using BI tools such as Tableau, Power BI, and Qlik to provide actionable insights to business users.
Bachelor's degree (minimum) in Computer Science, Data Engineering, Information Systems, Statistics, or a closely related technical field. Master's degree or relevant professional certification — such as Microsoft Certified:
Azure Data Engineer Associate, AWS Certified Data Analytics, or equivalent — is strongly preferred.
Minimum experience:
5 years of professional experience in data engineering, BI development, or a closely related data-intensive technical role. Demonstrated experience building and maintaining production-grade data pipelines and interactive dashboards in an organisational setting. Prior experience working with HR, workforce, or public sector data is a significant advantage. Experience supporting strategy, research, or policy teams with data infrastructure — not only operational or finance reporting — is highly desirable.
Skills — Required
- Advanced proficiency in SQL: complex queries, query optimisation, window functions, stored procedures, and schema design.
- Strong Python skills for data engineering tasks: data wrangling, pipeline scripting, API consumption, and automation (pandas, PySpark, or equivalent).
- Hands‑on experience with Power BI (preferred), Tableau, or Qlik — including advanced DAX measures, calculated columns, and report performance optimisation.
- Experience with cloud data platforms:
Azure (preferred — Azure Data Factory, Synapse Analytics, Azure SQL), AWS, or GCP. - Solid grounding in data warehousing concepts: dimensional modelling, star/snowflake schemas, slowly changing dimensions, and partitioning strategies.
- Experience with ETL/ELT orchestration tools such as Azure Data Factory, dbt, Talend, Informatica, or equivalent.
- REST API integration and handling of JSON/XML data formats.
- Exposure to predictive modelling or ML pipelines applied to workforce or organisational analytics.
- Familiarity with data cataloguing and governance tools (e.g., Microsoft Purview, Collibra, or equivalent).
- Experience managing multilingual or Arabic‑script datasets.
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