Lead Data Engineer / Lead Analytics Engineer

Job Category: Information Technology & Data Analytics

Job Type: Full-time, Permanent

Reference Number: SHO260825-13

Role Overview

The Shoprite Group is seeking an experienced Lead Data Engineer / Lead Analytics Engineer to guide the architecture, execution, and continuous optimization of enterprise data products at its Brackenfell technology hub.

Positioned at the intersection of core data engineering, advanced analytics, and business intelligence, this senior role focuses on translating complex operational requirements into scalable data pipelines, feature marts, and multidimensional data models.

As a technical leader within the enterprise data organization, you will champion modern analytics engineering standards, drive data governance, and mentor engineering talent. Your work will directly support high-impact machine learning models, executive reporting, and real-time decision-making across Africa's largest retail ecosystem.

Key Responsibilities & Operational Scope

Data Product Development & Analytics Engineering

• Scalable Data Architecture: Lead the design, build, and performance tuning of production-ready data marts, feature stores, and transformation pipelines to empower downstream data science and reporting teams.

• Complex Data Modeling: Apply dimensional modeling practices to structure unstructured and semi-structured retail data into reusable, business-ready models.

• Productization: Build robust end-to-end data products that serve predictive modeling, inventory forecasting, retail analytics, and customer loyalty frameworks.

Technical Leadership & Engineering Governance

• Code & Architecture Reviews: Direct code reviews, structural design evaluations, and deployment checks to ensure codebase integrity and operational efficiency.

• Best Practices & Standards: Establish software engineering principles within the data division, including version control workflows, automated testing frameworks, CI/CD integration, and thorough lineage documentation.

• Mentorship: Coach and upskill mid-level and junior analytics engineers, fostering technical excellence and continuous capability development.

Data Quality, Observability & Compliance

• Automated Data Quality: Implement data validation checks, monitoring alerts, and anomaly detection models across core pipelines to ensure high data reliability.

• Governance & Lineage: Maintain clear metadata, catalog definitions, data contracts, and transformation logic to adhere to enterprise compliance policies (including POPIA).

Cross-Functional Collaboration & Incident Operations

• Stakeholder Engagement: Partner with business analysts, enterprise architects, product owners, and data scientists to translate strategic retail objectives into technical roadmaps.

• Production Incident Resolution: Drive root-cause analysis for pipeline failures, data drift, or latency bottlenecks, deploying sustainable technical remediations.

Qualifications & Work Experience

Education

• Essential: Bachelor’s Degree or Higher Diploma in Computer Science, Data Engineering, Information Systems, Mathematics, Statistics, Software Engineering, or a related quantitative field.

• Alternative: Equivalent demonstrable experience in enterprise data engineering, data platform architecture, or analytics engineering.

Professional Experience

• Minimum Experience: 6+ years of hands-on experience in Data Engineering, Analytics Engineering, Data Science, or large-scale data modeling environments.

• Technical Leadership: Proven track record of guiding technical teams, defining architectural standards, and managing end-to-end deployment lifecycles.

• Domain Exposure: Prior experience within retail, e-commerce, supply chain, financial services, or high-volume transactional digital platforms is highly advantageous.

Core Technical Competencies

• Advanced SQL & Query Optimization: Deep expertise in writing, debugging, and tuning complex SQL queries for high-throughput database systems.

• Modern Data Stack & Cloud Architecture: Hands-on experience with cloud data platforms (e.g., Snowflake, BigQuery, AWS Redshift, Databricks) and modern transformation engines (e.g., dbt).

• Data Processing & Programming: Proficiency in Python, PySpark, or Scala for ETL/ELT development, data manipulation, and workflow orchestration.

• Data Modeling Frameworks: Expert-level mastery of Kimball dimensional modeling, Data Vault 2.0, star schemas, and feature store engineering for machine learning pipelines.

• DevOps for Data: Strong foundation in Git, automated integration/deployment (CI/CD) pipelines, containerization, and data pipeline observability platforms.

Why Join Shoprite Group’s Data Division?

Shoprite Group processes billions of customer transactions annually across thousands of retail stores and digital platforms like Sixty60. Joining the team as a Lead Data Engineer places you at the center of one of Southern Africa's largest, most sophisticated data operations, where your pipelines directly influence supply chain efficiency, customer personalization, and enterprise retail strategy.

Candidate Application Checklist

Before submitting your application via the Shoprite Group careers portal prior to 14 September 2026, ensure your resume highlights:

1. Tech Stack Specifics: Clearly outline your experience with cloud warehouses (Snowflake, Databricks, BigQuery), orchestration tools (Airflow, Dagster), and transformation tools (dbt).

2. Scale & Performance Metrics: Include quantifiable achievements such as query optimization speedups, pipeline reliability improvements, or dataset scale handled (e.g., terabytes/petabytes processed).

3. Leadership Impact: Detail specific examples of mentoring engineers, driving engineering standards, or leading major data product launches.