We are seeking a Senior Data Engineer to join a greenfield/future-state data platform initiative within the healthcare and medical scheme administration environment.
The successful candidate will be responsible for owning delivery end-to-end, from discovery and assessment through to implementation. This is a highly autonomous role requiring strong technical depth across both legacy data technologies and modern cloud data platforms.
The environment includes POPIA-sensitive health data, with a legacy estate incorporating Oracle, SAP BusinessObjects and SAS, while the organization transitions towards a modern cloud-based data environment.
Contract & Location
- Position: Senior Data Engineer
- Contract: Up to 18 months, structured in 6-month phases
- Start Date: 1 October 2026
- Work Model: Hybrid
- Location: Waterfall / Midrand / Johannesburg
Requirements
Key Technology Requirements
The ideal candidate should have strong hands-on experience with:
- Oracle Database
- Advanced SQL development and optimisation
- SAP BusinessObjects (BO)
- SAS
- Power BI
- Data profiling and quality assessment
- ETL and data engineering
- Process automation
- Self-service analytics
- Snowflake
- AWS
- Data modelling
- Metadata management and data cataloguing
- Generative AI / LLM applications
Role Summary
The Senior Data Engineer will operate as a self-sufficient technical lead, independently driving delivery and managing the engagement from discovery through implementation.
The primary objective is to reduce operational workload across the client's reporting estate through:
- Reporting rationalisation
- Process automation
- Self-service enablement
- Data quality automation
- Reusable data assets
- Improved governance and metadata management
Success will be measured by business outcomes, operational efficiency and released capacity, rather than simply utilisation.
Key Responsibilities
Discovery & Assessment
- Assess and baseline the current reporting landscape.
- Analyse complex and potentially undocumented data environments.
- Identify manual, repetitive and inefficient processes.
- Quantify effort and operational workload.
- Prioritise initiatives based on measurable effort reduction and business value.
Reporting Rationalisation
- Review the existing reporting estate.
- Identify opportunities to retire, consolidate and standardise reports.
- Establish consistent metric and data definitions.
- Reduce duplication and unnecessary reporting effort.
Data Engineering & Automation
- Design and implement robust ETL/data engineering solutions.
- Automate manual and repetitive processes.
- Develop automated validation, reconciliation and quality assurance controls.
- Implement appropriate audit trails and controls.
- Improve data reliability and operational efficiency.
Self-Service Analytics
- Develop reusable datasets and data products.
- Enable business users through governed Power BI assets.
- Promote appropriate self-service analytics while maintaining data governance.
Data Catalogue & Metadata
- Establish and maintain a centralised reporting/data catalogue.
- Define metadata standards.
- Improve discoverability and understanding of reporting assets.
- Explore appropriate use of Generative AI/LLMs for natural-language data and report discovery.
Stakeholder Management
- Engage directly with business and technical stakeholders.
- Shape requirements and scope.
- Drive technical and delivery decisions.
- Communicate progress, risks and outcomes effectively.
- Escalate only matters that genuinely require additional intervention.
Performance & Outcomes
- Define and track KPIs demonstrating reduced manual effort.
- Measure released capacity and operational improvements.
- Take ownership of planning, prioritisation and delivery across assigned workstreams.
Candidate Profile
The successful candidate will be:
- A highly experienced Senior Data Engineer with strong technical depth.
- Comfortable working independently with minimal supervision.
- Able to take ownership of an engagement from discovery through delivery.
- Experienced working within complex legacy data environments.
- Strong in Oracle, SQL, BusinessObjects and SAS.
- Comfortable working with modern cloud technologies such as AWS and Snowflake.
- Strong in data modelling, ETL, data quality and automation.
- Experienced with Power BI and self-service analytics.
- Able to navigate undocumented environments and quickly understand complex data estates.
- Commercially minded, with the ability to identify opportunities that remove operational effort.
- Confident engaging senior business and technical stakeholders.
- Comfortable working within a regulated healthcare and POPIA-sensitive environment.
Ideal Candidate Mindset
This role is suited to someone who does not need to be directed through each task.
The successful candidate should be able to walk into a complex environment, understand the current state, identify the biggest opportunities, develop a delivery plan and execute against measurable outcomes.
The focus is not simply on building data solutions, but on using data engineering, automation and modern analytics to simplify the reporting estate, reduce manual work and create sustainable business capacity.