Analytics Engineer - Intermediate

We are looking for an Intermediate Analytics Engineer with strong AWS experience, excellent SQL skills, and a solid understanding of ETL pipelines. You will bridge the gap between Data Engineers and Data Analysts by transforming complex data into accessible, reliable, and performant data models and insights that drive organizational decision-making.

Purpose Statement

To bridge the gap between Data Engineers and Data Analysts by transforming complex data into accessible, reliable, and performant data models and insights that drive organizational decision-making.

To collaborate with stakeholders to understand analytical needs, partnering with Data Architects and Data Modelers to design scalable data models aligned with enterprise strategy.

Design and build efficient, reproducible data assets (including data models and marts, semantic layer, working environments etc), along with scalable transformations and pipelines that empower both Analytical Engineers and Data Analysts to deliver insights and reports with speed, consistency, and minimal friction.

Key Performance Areas

1. Scope and responsibility

2. Influence and Stakeholder Engagement

3. Delivery

4. Technical/Specialist contribution and oversight

Key Tasks and Accountabilities Section 1

1. Scope and responsibility

1.1 Data Integration: Independently implement and maintain data transformations that structure and organize data within the managed data estate. Develop appropriate validation checks and testing frameworks for data quality assurance across moderately complex domains.

1.2 Data Warehouse: Apply intermediate data modelling techniques and SQL expertise to transform raw data into business-ready datasets. Create well-structured data models following dimensional modelling principles, with growing attention to performance optimization.

1.3 Analytics Engineering: Develop and maintain production-grade transformation code with thorough testing, appropriate version control practices, and comprehensive documentation. Contribute to reusable modelling components and help establish data standards. Data Visualization / Semantic Layer: Design and optimize intermediate data models for visualization and reporting. Create effective semantic layers and aggregation logic that address specific business needs while maintaining reasonable performance standards.

1.4 Data Model and Workload Support: Independently maintain and troubleshoot production data models of moderate complexity. Participate effectively in on-call rotation, investigate common model failures, address SQL performance issues, and follow established protocols for incident resolution.

1.5 Peer Support: Provide guidance to junior team members on foundational data modelling principles and basic SQL techniques. Participate constructively in code reviews and contribute to team knowledge sharing through documentation and informal coaching.

Key Tasks and Accountabilities Section 2

2. Influence and Stakeholder Engagement

2.1 Engagement: Engage with peers within the team, provide strategically focused advice to Data/Analytics Engineers, and collaborate with peer teams and Solutions Architects and Data Architects and Modelers.

2.2 Consensus Building: Drive consensus on design for other teams.

2.3 Leadership: Lead initiatives/teams concerning analytics engineering requirements and matters in the product/product line/ functional area.

2.4 Stakeholder Influence: Influence stakeholders regarding standards and solutions and influence the architecture/modeler during the design phase of data models and semantic artifacts.

3. Delivery

3.1 Standards and Planning: Set standards, plan, and direct the work of yourself and others; apply broad data engineering judgment to make and communicate the right trade-offs.

3.2 Problem Solving: Work on a varied range of systems and design approaches to solve problems and deliver on requirements with limited guidance from others.

3.3 Quality and Efficiency: Produce high-quality products quickly and efficiently, with designs that stand the test of time.

3.4 Impediment Removal: Remove impediments and inefficiencies to enable team members and peers in other teams to deliver solutions more quickly.

Key Tasks and Accountabilities Section 3

4.Technical/Specialist contribution and oversight

4.1 Design and Development: Design, develop, test, and deploy scalable implementations of data models and semantic layer artifacts according to analytics engineering standards and best practices, guiding and supporting others to do so.

4.2 Complex Patterns: Work with and extend nonstandard/complex patterns.

4.3 Technical Trade-offs: Make medium-term and strategic as well as tactical technical trade-offs.

4.4 Efficiency and Speed: Drive efficiency and improved speed through architecture engineering, testing, operational excellence, and best practices within a team. Be able to optimize poorly running processes.

4.5 Continuous Improvement: Monitor and measure for continuous improvement. Utilise AI and other modern tooling and approaches to optimise team productivity.

4.6 Mentorship: Mentor and guide less experienced individuals within the immediate product or functional area.

Experience

Length of experience required is conditional on the qualifications obtained but must include:

Experience in analytics engineering or related roles.

Proven track record of delivering scalable data solutions and leading projects.

Experience with CI/CD, testing frameworks, and orchestration tools (e.g., Airflow).

Experience with productionising reports using engineering principles.

Qualifications (Minimum)

Bachelor's Degree in Analytical/Data/Technical or Other

Qualifications (Ideal or Preferred)

Honours Degree in Analytical/Data/Technical or Engineering - Other

Knowledge Advanced grasp of:

Data modelling best practices.

Data governance and quality assurance.

Cloud data platforms and orchestration tools (e.g., Airflow, Prefect).

Understanding of software engineering principles (e.g., CI/CD, testing).

Skills

Analytical Skills

Communications Skills

Planning, organising and coordination skills

Problem solving skills

Reporting Skills

Competencies

Attract and relentlessly develop people (Departmental Contributor)

Care and passion for our people (Departmental Contributor)

Drive innovation mindset (Departmental Contributor)

Earn and extend trust (Departmental Contributor)

Inspire optimism and persistence (Departmental Contributor)

Lead with the "Why" (Departmental Contributor)

Make decisions - faster and smarter (Departmental Contributor)

Simplify and make it easy (Departmental Contributor)

This role is designated for Employment Equity candidates as per the client’s EE plan.

This is a contract opportunity with our client with the option of converting to a permanent employee at the end of the contract.