{"id":703833,"url":"https://alion.io/job/takealot-data-principal-engineer","title":"Data Principal Engineer","company":{"id":57341,"name":"Takealot","domain":"takealot.com","url":"https://alion.io/company/takealot","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":84,"open_postings":32,"ghost_share":0,"stale_share":0.656,"repost_share":0,"time_to_fill_p50_days":53,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cape Town, South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":34000,"max_usd":76000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":662},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"BigQuery","optional":false},{"name":"Feature Store","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Looker","optional":false},{"name":"RAG","optional":false},{"name":"Apache Kafka","optional":true},{"name":"CI/CD","optional":true},{"name":"dbt","optional":true},{"name":"GCP","optional":true},{"name":"Kotlin","optional":true},{"name":"Kubernetes","optional":true},{"name":"LookML","optional":true},{"name":"Machine Learning","optional":true},{"name":"Python","optional":true},{"name":"Redis","optional":true},{"name":"Scala","optional":true},{"name":"Slack","optional":true},{"name":"SQL","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-07-14T14:19:43Z","employer_posted_date":"2026-08-17","last_verified_at":"2026-10-18T06:52:53Z","board_verified":true,"closed_at":null,"days_open":95,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":95},"description":"Takealot.com, South Africa’s leading online retailer, is looking for a highly talented Data Principal Engineer to join our team based in Cape Town.\nWe are a young, dynamic, hyper-growth company looking for smart, creative, hard-working people with integrity to join us!\nThink you’ve been challenged before? Think again!\nScale: 4 million happy shoppers shop online on takealot.com. Show them what you can do.\nLearn: We work with the best of the best, and then some. Code alongside industry leaders and up-skill in record time.\nGrow: Expand your career in the fast-growing Takealot Group:takealot.com, Mr D and TFS. We like to promote from within: Here’s your chance.\nPurpose of the role: \nOur data and analytics platform powers the operational backbone of Takealot Group, from logistics and supply chain to distribution centre analytics and data monetisation products. It's grown fast, and the business increasingly needs real-time operational data that our historically batch-oriented platform wasn't built for.\nAs 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 Group's data governance programme runs on, and design the automation that keeps governance and platform operations manageable as the Group scales. You'll leave behind an architecture the team can understand, build on, and be proud of.\nThis 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.\nKey Responsibilities: \nEcosystem Architecture & Platform Simplification: \nProduce a definitive, up-to-date master blueprint of the Group's 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.\nReal-Time Operational Enablement: \nLogistics, Supply Chain, and Distribution Centre operations need faster access to operational data than our current batch-oriented warehouse provides. \nDesign 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.\nData Governance Standards & Documentation:\nEstablish and own the technical standards that underpin the Group's data governance programme. \nDefining data quality standards, lineage documentation requirements, and data management practices, and building them into sprint workflows as normal engineering practice.\nWorking with central team SMEs (Data Engineering, Analytics Engineering, BI, DataOps) to turn existing engineering practice into formal, Group-level domain standards.\nBuilding and maintaining a centralised architecture repository as the Group's 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 repository's structure and the first few highest-priority domains, with full coverage growing over the following quarters.\nKeeping standards and documentation current as the platform evolves.\nAI Enablement, Integration & Automation Platform:\nDefine 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.\nOwn 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.\nLead integration planning for AI tooling on the platform, secure, well-governed connection patterns for AI agents, in line with the Group's AI Data Policy.\nDesign 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.\nTechnical Onboarding, Training & Knowledge Transfer:\nReplace fragmented, course-heavy onboarding with a structured learning path and reference architecture guides tailored to different skill levels (engineering, analytics, BI). \nDesign specialist technical training modules covering architecture and standards, and contribute content into the Group's tiered governance training. \nSuccess here means the team can operate without depending on any one person's knowledge.\nTechnical Leadership & Mentorship:\nAs 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. \nWhere a technical recommendation conflicts with a team's delivery priorities, the accountable manager makes the final call, your job is to bring the strongest technical case to that decision. \nYou 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.\n Minimum Required Qualification:\nA Bachelor's 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.\n Minimum Required Experience:\n8+ years experience in data engineering, including at least 3 years at principal or architect level in a complex, high-scale environment.\nA track record designing and delivering large-scale data platforms: lakehouse/warehouse, real-time event streaming, data ingestion frameworks.\nExperience leading platform simplification or technical debt reduction, not just greenfield builds.\nExperience contributing technical standards into a formal data governance programme (DMBOK familiarity a plus).\nHands-on experience with AI or ML data infrastructure: feature stores, model serving pipelines, data contracts, drift monitoring.\nExperience 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.\nA track record of building automation or internal tooling, dashboards, scripts, copilots that cuts manual operational work.\nExposure to logistics, e-commerce, or supply chain data is a plus. \n Technical Skills:\nDeep GCP expertise, particularly BigQuery and Dataform.\nStrong experience with stream processing frameworks (Kafka, Pub/Sub, or equivalent).\nSolid command of dimensional and event-driven data modelling; working knowledge of Looker/LookML.\nExperience with Infrastructure as Code (Terraform or equivalent) and CI/CD for data pipelines.\nStrong Python and SQL; familiarity with dbt-style transformation frameworks and orchestration tools.\nUnderstanding of POPIA obligations as they apply to data processing and governance.\nArchitecture & Governance:\nCan produce clear architecture documentation: data flow diagrams, ADRs, technical blueprints, onboarding guides.\nExperience designing for auditability, data lineage tracing, and compliance requirements.\nComfortable weighing build vs. buy trade-offs and driving technology standardisation across teams.\nLeadership & Communication:\nComfortable acting as the final technical authority in a data domain - setting standards, making calls, bringing teams along.\nExperience advising senior stakeholders and translating technical trade-offs into business terms.\nGenuinely invested in growing the people around you, through mentorship and knowledge-sharing.\nPragmatic: understands the best architecture is the one the team can actually run.\nLet’s talk about life @ Takealot Group\nThe 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.\nNo doors: We aren’t fans of stuffy offices or siloed work environments. See someone you like? High five, collab and make something great.\nRemote working: Love water cooler chats and working from home? Takealot.com offers a hybrid working model for the best of both worlds.\nMentorship programme: We aren’t kidding when we say the people with the best people win. Now’s your chance to be one of the best by learning from the best.\nNaspers 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.\n Good times: Get to know the other extraordinary minds at takealot.com during regular social events and out-of-office activities (think hikes, think mini golf, think good times)\nStaff discount: Millions of products across 28 departments. What more could you ask for?\nBirthday leave: Cake Day all the way. Enjoy your day off - you deserve it.\nRight tools, right job: You’ll work on the latest tech, of the latest tech.\nHelp when you need it most: Confidential counselling, legal support, and financial guidance, for free, anytime, anywhere.\n 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.\n Free parking: No more 5km fun runs to your desk (unless you want to).\nLike what you see? If you meet the above you are an Extraordinary Mind. Apply today!!\nTakealot is an Equal Opportunity Employer. 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