Senior Data Engineer

Senior Data Engineer
Location: Stellenbosch (remote/hybrid, optional one day a week in office)
Type: Permanent
Experience: Senior level

About the role
We’re working with an early-growth analytics company that turns high-frequency data into demand forecasts leaders can actually act on. They’re looking for a Senior Data Engineer to design and build the data infrastructure at the heart of their forecasting platform.
This is a hands-on individual contributor role with genuine technical authority. You’ll assess what exists, decide what needs to change, and build it, with direct support from the wider tech team. The data is fundamentally time series in nature: high-frequency transactional data enriched with contextual and reference datasets that have to be modelled, integrated, and kept current across multiple clients and locations. The data science team works primarily in R. The cloud stack is GCP throughout.
You’ll bring rigour, structure, and engineering discipline to the data layer: decoupling ETL from compute, establishing lineage and documentation, and building pipelines the team can depend on and reason about. The people you’ll work with are sharp and expert in their own domains, and some will need persuading that engineering discipline is worth the short-term friction. You’ll need to make that case clearly and hold it.

What you’ll do
• Assess existing data infrastructure across core projects and produce a clear, actionable view of what to keep, change, and how
• Design and implement a data warehouse architecture supporting analytics and ML workloads, including dimensional modelling, data contracts, and schema standards
• Decouple ingestion and transformation from compute, building automated, production-ready pipelines across heterogeneous sources: flat files, cloud storage, databases, and APIs
• Establish data lineage across core projects: sources, schedules, transformations, storage, schemas, versioning, change tracking, and access controls
• Audit and establish clear data isolation between clients, identifying risks and implementing controls
• Implement pipeline orchestration using a DAG-based framework, managing scheduled ingestion and multi-tenant data flows
• Own the full data flow, making sure data arrives clean and well-documented for data science, and that their output is delivered reliably to where it needs to go
• Define and enforce data quality, monitoring, and observability as first-class concerns, with attention to freshness for time series workloads
• Implement and maintain CI/CD pipelines for data workflows, with environment separation and repeatable deployments
• Own your own infrastructure: containerize and deploy your pipelines and provision what you need using Terraform or equivalent IaC
• Set data engineering standards and maintain clear documentation so the broader team can work with the data layer independently
• Design and operate infrastructure with cost efficiency in mind as data volumes and the client base grow

What you’ll bring
Essential
• Proven experience designing and building production data pipelines across heterogeneous sources, batch and scheduled ingestion at minimum
• Strong, production-proven experience with industry-standard DAG-based orchestration frameworks
• Strong Python and SQL applied to real data engineering problems in production
• Solid understanding of data warehouse design and dimensional modelling, including data contracts and slowly changing dimensions
• Practical understanding of lineage, schema management, data quality, versioning, and access control
• Understanding of the data structures and freshness needs underpinning time series ML and analytics
• Experience with multi-tenant data architectures and their security and isolation considerations
• Solid DevOps capability: you containerize your own work and manage your own deployments
• Strong CI/CD experience for data workflows, including environment separation
• Solid experience with Terraform or equivalent IaC
• Strong GCP experience (GCS, BigQuery, Cloud Run); strong AWS candidates who can move between platforms will also be considered, though GCP is where you’ll work
• The ability to assess an unfamiliar system quickly and make a principled case for change
• Strong communication across technical and non-technical audiences

Desirable
• Experience introducing data engineering discipline into a team that didn’t previously have it
• Exposure to near-real-time ingestion/processing patterns
• Experience with dbt or similar transformation and documentation frameworks
• Exposure to data observability tooling
• Experience working alongside data science or ML teams in production
• Background in B2B SaaS or analytics products

What matters most
• You’re naturally curious and self-directed; when you hit an unfamiliar problem, your instinct is to work through it, not around it
• You’re comfortable being the most knowledgeable person in the room in your domain, and you know that sitting with a hard problem isn’t a sign something’s wrong
• You build data systems people can trust, understand, and reason about
• You form strong technical opinions, communicate them clearly, and know how to make the case for doing things properly
• You’re motivated by building from a solid foundation rather than patching over accumulated shortcuts
• A degree is required (Honours/Masters ideal)