Data Principal Engineer

South Africas leading online retailer is looking for a highly talented Data Principal Engineer to join our team based in Cape Town.

We are a young dynamic hyper-growth company looking for smart creative hard-working people with integrity to join us!

Think youve been challenged before Think again!

• Scale: 4 million happy shoppers shop online on . Show them what you can do.
• Learn: We work with the best of the best and then some. Code alongside industry leaders and up-skill in record time.
• Grow: Expand your career in the fast-growing Takealot Group: Mr D and TFS. We like to promote from within: Heres your chance.

Purpose of the role:

Our data and analytics platform powers the operational backbone of Takealot Group from logistics and supply chain to distribution centre analytics and data monetisation products. Its grown fast and the business increasingly needs real-time operational data that our historically batch-oriented platform wasnt built for.

As Data Principal Engineer you are the highest individual-contributor technical authority in the data division. The mandate: simplify the ecosystem build the real-time data layer the business now needs set the technical standards the Groups data governance programme runs on and design the automation that keeps governance and platform operations manageable as the Group scales. Youll leave behind an architecture the team can understand build on and be proud of.

This is a transformation role with executive sponsorship direct business impact and real autonomy over consequential technical decisions. This is a formalised senior individual contributor milestone on our technical career ladder a long-term seat for strong engineers who want to keep growing technically without moving into people management.

Key Responsibilities:

Ecosystem Architecture & Platform Simplification:

• Produce a definitive up-to-date master blueprint of the Groups data architecture sources flows models KPI mappings and use it to drive a structured simplification programme; cutting over-engineering and technical debt standardising technology choices across teams and making it faster to deliver new data products.

Real-Time Operational Enablement:

• Logistics Supply Chain and Distribution Centre operations need faster access to operational data than our current batch-oriented warehouse provides.
• Design and build the event-driven live data layer that decouples these systems from the historical reporting warehouse enabling faster and more precise operational analytics across the Group.

Data Governance Standards & Documentation:

• Establish and own the technical standards that underpin the Groups data governance programme.
• Defining data quality standards lineage documentation requirements and data management practices and building them into sprint workflows as normal engineering practice.
• Working with central team SMEs (Data Engineering Analytics Engineering BI DataOps) to turn existing engineering practice into formal Group-level domain standards.
• Building and maintaining a centralised architecture repository as the Groups single source of truth for how data flows across the ecosystem. Given the scale involved hundreds of systems across 15-20 business units. This is a phased build: the first 90 days should produce the repositorys structure and the first few highest-priority domains with full coverage growing over the following quarters.
• Keeping standards and documentation current as the platform evolves.

AI Enablement Integration & Automation Platform:

• Define the technical guardrails that let teams innovate safely: data contracts security and privacy controls model governance patterns and clear standards for embedding automation into data engineering operations.
• Own platform alignment for AI consumption so BigQuery Dataform and Looker expose data that AI tools and copilots can use reliably and safely: documented schemas semantic layers data contracts consistent access patterns.
• Lead integration planning for AI tooling on the platform secure well-governed connection patterns for AI agents in line with the Groups AI Data Policy.
• Design and build automation that keeps governance and platform operations manageable as the Group scales automated maturity telemetry from platform metadata (classification tag coverage lineage completeness Dataform test coverage Looker documentation completeness) self-service onboarding tooling and AI-assisted copilots that cut manual facilitation work across the Group.

Technical Onboarding Training & Knowledge Transfer:

• Replace fragmented course-heavy onboarding with a structured learning path and reference architecture guides tailored to different skill levels (engineering analytics BI).
• Design specialist technical training modules covering architecture and standards and contribute content into the Groups tiered governance training.
• Success here means the team can operate without depending on any one persons knowledge.

Technical Leadership & Mentorship:

• As the highest individual contributor in the data domain you weigh in on design disagreements unblock the most complex cross-functional challenges and set the engineering standard for the division.
• Where a technical recommendation conflicts with a teams delivery priorities the accountable manager makes the final call your job is to bring the strongest technical case to that decision.
• You coach and mentor senior and staff-level engineers advise the Engineering Director on platform and governance strategy and contribute to the technical career path matrix for the department.

Output:

• By 3 months you should have an initial ecosystem audit and a first-cut master architecture blueprint a formal technical design for the live operational data layer ready for approval and the first set of Group-level domain standards drafted with central team SMEs. Youll also have scoped the AI guardrails and put together a prioritised automation build plan; the telemetry tooling and copilot work that makes the rest of this role sustainable.
• By 6 months the operational data layer should be live for Logistics Supply Chain and DC analytics with AI adoption guardrails finalised and adopted. The architecture repository should be live with the initial priority domains documented and a coverage roadmap agreed for the rest. Automated governance telemetry should be running for those same priority domains a structured onboarding path should be in active use and the technical career progression matrix should be published.
• Some of this will still be in motion past 6 months thats expected given the scale of the ecosystem. What should be steadily true on an ongoing basis: complexity reduction work measurably reclaiming engineering capacity governance standards kept current as the platform evolves governance and onboarding overhead staying low because of automation and self-service tooling and architectural standards documented and accessible across the team.

Minimum Required Qualification:

• A Bachelors degree in Computer Science Engineering Information Systems or a related field is preferred; equivalent demonstrated experience at this scale and seniority will be considered in place of formal qualifications.

Minimum Required Experience:

• 8 years experience in data engineering including at least 3 years at principal or architect level in a complex high-scale environment.
• A track record designing and delivering large-scale data platforms: lakehouse/warehouse real-time event streaming data ingestion frameworks.
• Experience leading platform simplification or technical debt reduction not just greenfield builds.
• Experience contributing technical standards into a formal data governance programme (DMBOK familiarity a plus).
• Hands-on experience with AI or ML data infrastructure: feature stores model serving pipelines data contracts drift monitoring.
• Experience integrating AI/LLM tooling (copilots agents RAG) with a data platform access patterns semantic layers and data contracts that let AI tools consume data safely.
• A track record of building automation or internal tooling dashboards scripts copilots that cuts manual operational work.
• Exposure to logistics e-commerce or supply chain data is a plus.

Technical Skills:

• Deep GCP expertise particularly BigQuery and Dataform.
• Strong experience with stream processing frameworks (Kafka Pub/Sub or equivalent).
• Solid command of dimensional and event-driven data modelling; working knowledge of Looker/LookML.
• Experience with Infrastructure as Code (Terraform or equivalent) and CI/CD for data pipelines.
• Strong Python and SQL; familiarity with dbt-style transformation frameworks and orchestration tools.
• Understanding of POPIA obligations as they apply to data processing and governance.

Architecture & Governance:

• Can produce clear architecture documentation: data flow diagrams ADRs technical blueprints onboarding guides.
• Experience designing for auditability data lineage tracing and compliance requirements.
• Comfortable weighing build vs. buy trade-offs and driving technology standardisation across teams.

Leadership & Communication:

• Comfortable acting as the final technical authority in a data domain setting standards making calls bringing teams along.
• Experience advising senior stakeholders and translating technical trade-offs into business terms.
• Genuinely invested in growing the people around you through mentorship and knowledge-sharing.
• Pragmatic: understands the best architecture is the one the team can actually run.

Lets talk about life @ Takealot Group

• The power is in your hands: We offer a market-related Total Remuneration Package that allows full flexibility according to your needs. Go on be the master of your own destiny.
• No doors: We arent fans of stuffy offices or siloed work environments. See someone you like High five collab and make something great.
• Remote working: Love water cooler chats and working from home offers a hybrid working model for the best of both worlds.
• Mentorship programme: We arent kidding when we say the people with the best people win. Nows your chance to be one of the best by learning from the best.
• Naspers Tech Community & Online Learning: Share ideas and grow with global industry leaders who are all just a Slack message away. Love to learn Upskill with free access to courses on MyAcademy Udacity Coursera and more.
• Good times: Get to know the other extraordinary minds at during regular social events and out-of-office activities (think hikes think mini golf think good times)
• Staff discount: Millions of products across 28 departments. What more could you ask for
• Birthday leave: Cake Day all the way. Enjoy your day off - you deserve it.
• Right tools right job: Youll work on the latest tech of the latest tech.
• Help when you need it most: Confidential counselling legal support and financial guidance for free anytime anywhere.
• Tech stack (for days): ReactJS Python Scala Kotlin Swift Google Cloud Kafka Redis Kubernetes and all things machine learning. If you build it they will come.
• Free parking: No more 5km fun runs to your desk (unless you want to).

Like what you see If you meet the above you are an Extraordinary Mind. Apply today!!

Takealot is an Equal Opportunity Employer. Applicants from previously disadvantaged groups and people with disabilities will be given preference.