South Africa: Data Engineer

Key Responsibilities: Data Engineering & Pipeline Management Design, build, and optimize T-SQL stored procedures, functions, and scripts for high-volume data processing and ECM scoring. Develop, deploy, and monitor end-to-end ETL/ELT workflows (e.g., SQL Server Agent, SSIS, Azure Data Factory, or Airflow) with checkpoint/rollback, job tracking, and recovery capabilities. Perform data cleansing, preparation, and transformation to support business intelligence and machine learning workflows. Engineer and maintain reusable feature store tables (per entity/tax type) for ML models and operational scoring. Model and maintain data warehouse structures (3NF, dimensional/star/snowflake), ensuring proper documentation of data lineage. Prepare and deliver curated, scored datasets for downstream consumption in Power BI dashboards and analytics environments. Develop and maintain audit, telemetry, and job tracking tables to ensure data reliability, restartability, and monitoring visibility. Support and troubleshoot production pipelines, optimizing query performance via indexing, tuning, and profiling tools. Data Quality, Governance, and Compliance Implement and monitor data validation, reconciliation, and QA frameworks across the data lifecycle. Enforce data security, privacy, and compliance controls in line with corporate and regulatory standards. Support the implementation of data governance and lineage documentation, ensuring traceability and adherence to EDM policies. Collaboration and Cross-functional Support Collaborate with data analysts, data scientists, software engineers, and business stakeholders to translate business problems into scalable data solutions. Provide accessible, well-documented datasets to support analytics and reporting. Contribute to all phases of the SDLC, including requirements, design, development, testing, deployment, and maintenance. Qualifications and Experience: A tertiary qualification in Computer Science, Information Systems, Data Engineering, Analytics, Mathematics, or Statistics or Matric with 6-8 years of experience in data engineering, database development, or data management in production environments. Proven hands-on experience with SQL Server, including advanced T-SQL development, ETL/ELT workflow design, and performance tuning. Demonstrated delivery of production data solutionsboth batch and near real-timewithin enterprise environments. Experience in building and maintaining data warehouses, feature stores, and reusable data products. Track record of implementing data governance and quality frameworks, ensuring compliance and traceability. Experience in orchestrating complex data pipelines using SQL Server Agent, SSIS, Airflow, or Azure Data Factory. Familiarity with cloud-based data architectures (Azure preferred) and version control systems (Git). Exposure to Power BI or equivalent visualization tools for reporting and analytics enablement. Strong understanding of data security, privacy, and regulatory compliance requirements. Key Skills and Competencies: Advanced SQL Server Development: Strong proficiency in T-SQL, stored procedure design, query optimization, indexing, and error handling. ETL and Data Warehousing: Expertise in ETL/ELT pipeline design and orchestration for batch and near real-time processing using SQL Server Agent, SSIS, or Azure Data Factory. Data Modeling: Solid understanding of normalized and dimensional modeling (3NF, star, snowflake) and scalable architecture design. Feature Store Development: Ability to design and maintain reusable feature tables supporting machine learning and operational scoring. Data Validation and Quality Assurance: Skilled in implementing validation rules, reconciliation checks, and QA frameworks to ensure data integrity. Data Governance and Security: Strong knowledge of data governance, privacy, and compliance standards; experience maintaining data lineage documentation. Workflow Orchestration: Experience building restartable, traceable workflows with checkpoint and rollback mechanisms. Programming and Scripting: Proficiency in SQL and beneficial experience in Python or R for automation and data manipulation. Cloud Platforms: Familiarity with Azure (preferred) or other cloud platforms such as AWS or GCP for data engineering workloads. Version Control and CI/CD: Exposure to Git and CI/CD pipelines for managing data workflow deployment. Visualization and Reporting (Beneficial): Ability to prepare scored or curated data for BI tools such as Power BI. Performance Optimization: Expertise in performance tuning, query profiling, and indexing strategies to optimize large-scale data operations. Collaboration and Communication: Ability to work effectively across technical and business teams, translating complex requirements into practical data solutions.