Senior Data Engineer – Healthcare Data Platform

Blue Pearl HQ; Johannesburg; Hybrid; ZAR 1,000,000 - 1,500,000; Full time

Job summary

Blue Pearl HQ is seeking a Senior Data Engineer to lead delivery from discovery through implementation in a hybrid environment.

You will reduce operational workload across reporting estates by automating data processes, rationalising reports, and building self-service analytics with Power BI on AWS/Snowflake.

Qualifications

• Requires extensive experience with Oracle, SQL, BO and SAS.
• Experience designing robust ETL/data pipelines and automated processes.
• Proficiency in cloud platforms (AWS) and modern analytics tooling.

Responsibilities

• Lead discovery and assessment of client data environments.
• Drive reporting rationalisation, standardisation and performance improvements.
• Design and implement robust ETL/data engineering solutions and automation.
• Develop governed self-service analytics using Power BI.
• Establish centralized metadata and data catalogue standards.
• Engage stakeholders to shape requirements and delivery plans.
• Define metrics to track reduced manual effort and capacity gains.

Skills

Oracle Database

Advanced SQL

SAP BO

SAS

Power BI

Data profiling

ETL

Process automation

Self-service analytics

Snowflake

AWS

Data modelling

Metadata management

Generative AI/LLMs

Job description

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

• 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

• 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.