Senior Data & ML Engineer

OutsideCapital; Wes-Kaap; On-site; ZAR 1,000,000 - 1,700,000; Full time

Job summary

OutsideCapital is seeking a hands-on technical leader to shape foundational data architecture across our growing data-engineering team in Cape Town. You will define ingestion, integration, modeling and governance, creating reusable patterns and guiding production decisions.

The role emphasizes setting direction, explaining trade-offs and solving the hardest problems while remaining close to delivery work and mentoring engineers at earlier career stages.

Qualifications

• Degree in computer science, information systems, engineering, mathematics or related technical discipline.
• Typically 7–10 years in senior or lead data engineering/platform development.
• Proven ownership of data-platform or enterprise-integration architecture across multiple products.
• Strong Python, SQL, ETL/ELT, data-modelling and database-engineering foundations.
• Strong ML-system and MLOps architecture depth: reproducibility, deployment, monitoring, lifecycle controls.
• Evidence of defining CI/CD, testing, release, rollback and reliability patterns.
• Mentor engineers at earlier career stages; distinguish personal ownership from delegated work.
• Azure or Microsoft-stack exposure is useful; experience with AVEVA PI/Asset Framework or Airflow is advantageous.

Responsibilities

• Define how data is ingested, integrated, modeled and governed across products.
• Create reusable patterns and raise engineering discipline for production changes.
• Lead architecture decisions, explain trade-offs, and guide difficult cross-team problems.
• Stay close to the work to solve hard technical problems while setting direction.
• Mentor engineers and foster ownership and review processes.

Skills

Python

SQL

ETL/ELT

Data modelling

CI/CD

MLOps

ML-system architecture

Azure

Airflow

Prefect

TensorFlow/PyTorch

Education

Bachelor's degree in Computer Science/Information Systems/Engineering

Tools

Airflow

Prefect

Azure Data Factory

TensorFlow

PyTorch

Job description

You will bring foundational architecture judgement to a growing data-engineering team. You will define how data is ingested, integrated, modelled, governed and made ready for analytics and ML across connected products. You will create patterns that other engineers can reuse, improve the discipline around production change, and guide difficult decisions that span systems and stakeholders.

This is a hands-on technical leadership role: the expectation is not to do every delivery task personally, but to set the right direction, explain the trade-offs, raise the engineering bar and remain close enough to the work to solve the hardest problems.

Profile for Success

• A degree in computer science, information systems, engineering, mathematics or a related technical discipline.
• Typically 7–10 years in senior or lead data engineering / platform development. Candidates with fewer years may be considered where they can demonstrate equivalent platform scope, architecture ownership and production judgement.
• Proven ownership of data-platform or enterprise-integration architecture across multiple connected products, not only participation in individual pipelines.
• Strong Python, SQL, ETL/ELT, data-modelling and database-engineering foundations, with the ability to explain design choices in terms of cost, latency, reliability, security and maintainability.
• Strong ML-system and MLOps architecture depth: reproducibility, deployment, monitoring, lifecycle controls and responsible-use considerations.
• Evidence of defining CI/CD, testing, release, rollback and reliability patterns that other engineers adopted.
• Demonstrated ability to distinguish personal ownership from delegated work, seek appropriate review and mentor engineers at earlier career stages.
• Azure or Microsoft-stack exposure is useful. Operational, industrial, IoT, asset-data or energy experience is a differentiator, not a sector gate; AVEVA PI, Asset Framework, CAMs, Airflow, Prefect, Azure Data Factory, TensorFlow or PyTorch are advantageous rather than all mandatory.
• Cape Town-based availability and willingness to travel occasionally to operational sites, subject to final role-process confirmation.