DATA ENGINEER – Old Mutual 7 views

Key Responsibilities

• Data Pipeline Development & Maintenance -Design, develop, and maintain end-to-end data ingestion pipelines (using Python and other relevant technologies) to move data from various source systems into Delta Bricks and the Data Lake. -Create and manage ETL/ELT processes to curate data through Bronze, Silver, and Gold layers, ensuring consistent data quality and integrity.
• Data Quality & Governance -Implement robust validation, cleansing, and reconciliation checks to ensure accuracy and completeness of data across all layers. -Collaborate with Actuarial teams to define data quality standards and uphold governance policies within the data pipeline.
• Code & Script Management -Write, review, and maintain Python scripts (or other relevant code) used for data extractions, transformations, and loading processes. -Manage version control and continuous integration/continuous deployment (CI/CD) practices for all data engineering codebases.
• Performance Monitoring & Optimization -Monitor data pipeline performance to identify bottlenecks and optimize code for maximum throughput, scalability, and fault tolerance. -Conduct capacity planning and implement performance tuning measures.
• Collaboration & Stakeholder Engagement -Partner with Actuarial Analysts, Data Scientists, and other business stakeholders to understand data requirements and propose solutions that meet actuarial and reporting objectives. -Coordinate with platform teams to ensure alignment with enterprise data architecture, security, and compliance standards.
• Documentation & Knowledge Sharing -Maintain thorough documentation of all data flows, data models, processes, and system configurations. -Provide technical guidance, training, and best practices to team members and business partners as needed.
• Incident Management & Continuous Improvement -Troubleshoot and resolve production issues or data anomalies in a timely manner. -Continuously refine and enhance the data engineering environment to maintain high-quality data for actuarial use cases.

Requirements

• Education: -Bachelor’s Degree in Actuarial Science, Computer Science, Information Technology, or a related field.
• Work Experience: -3+ years’ professional experience in data engineering, data integration, or equivalent roles. -Proven experience building, optimizing, and maintaining data pipelines and architectures (e.g., ETL/ELT in a Data Lake environment).
• Technical Skills: -Proficiency in Python for data processing, scripting, and automation. -Experience with data lake technologies and data lifecycle management (Bronze, Silver, Gold layers or similar). -Strong SQL skills for querying and transforming large datasets. -Familiarity with version control tools (e.g., Git) and CI/CD pipelines. -Knowledge of cloud-based data platforms, containerization, or orchestration frameworks (e.g., Azure, AWS, Kubernetes, Airflow) is an advantage.
• Other Requirements: -Solid understanding of data governance and data quality best practices. -Ability to manage multiple priorities and deadlines in a fast-paced environment. Skills Application Development, Barcodes, Data Architecture Development, Data Compilation, Data Engineering, Data Modeling, Digital Literacy, Evaluating Information, Graphical User Interface (GUI) Development, Information Technology (IT) Support, IT Installations, PHP (Programming Language), Presentation Design, Product Development, Software Development Life Cycle (SDLC) Methodologies, Software Testing, SQL Database Queries, Structured Query Language (SQL), Test Case Management, User Experience (UX) Design, Web Platform Development Software
• Competencies Business Insight Communicates Effectively Cultivates Innovation Customer Focus Decision Quality Drives Results Ensures Accountability Manages Ambiguity
• Education NQF Level 7 – Degree, Advance Diploma or Postgraduate Certificate or equivalent

Salary

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