Head Of Data Engineering

Kifiya Financial Technology; Cape Town; On-site; ZAR 1 200 000 - 1 600 000; Full time

Job summary

A financial technology firm in Cape Town is seeking a Head of Data Engineering to lead the data engineering strategy, mentor teams, and oversee scalable architectures. The ideal candidate will have 8–10 years of experience in data engineering, 5 of which should be in a leadership role. Advanced knowledge of SQL, Python, and data governance is essential, along with a relevant degree. This position offers the chance to work in a dynamic environment focused on innovation and data excellence.

Qualifications

• Minimum 8–10 years of experience in data engineering or analytics.
• At least 5 years in a leadership capacity.
• Proven leadership in managing data engineering & BI teams.

Responsibilities

• Define and execute the Data Engineering strategy.
• Lead and mentor Data Engineers and BI Developers.
• Design and oversee scalable data architectures.

Skills

Data engineering leadership

Cloud data architectures

SQL

Python

Data governance

Data warehousing

Education

Bachelor’s or Master’s degree in Computer Science, Information Systems or Data Engineering

Tools

Snowflake

Apache Kafka

Apache Airflow

Power BI

Job description

Job title: Head of Data Engineering

Job Location: Western Cape, Cape Town

Deadline: December 18

What You’ll Do

Define and execute the Data Engineering strategy aligned with IDD and enterprise data goals.

Lead and mentor high‑performing teams of Data Engineers and BI Developers to deliver enterprise‑grade solutions.

Collaborate closely with the CDO, Chief of IDD, and department leads to ensure data infrastructure supports business and analytical requirements.

Champion the vision of data as a product, enabling reusable, governed, and high‑quality data assets.

Design and oversee scalable, secure, and automated data architectures supporting both batch and real‑time processing.

Manage and optimise data pipelines, data lakes, warehouses and streaming systems (e.g., Presto, StarRocks, Snowflake, ClickHouse).

Partner with Platform Engineering and AI teams to integrate data infrastructure with MLOps, API services and advanced analytics.

Ensure data lineage, versioning and cataloguing through integration with governance and metadata systems.

Lead the development of data ingestion frameworks from multiple internal and external sources (APIs, databases, third‑party feeds).

Implement ETL/ELT pipelines that are efficient, reliable and easily scalable.

Work with the Data Governance team to ensure data quality, integrity and standardisation across all domains.

Introduce automation and testing practices to minimise manual data handling and improve consistency.

Oversee BI and data visualisation development, ensuring timely and accurate delivery of dashboards, reports and insights.

Partner with business and risk stakeholders to design data models and metrics frameworks aligned to KPIs.

Support self‑service analytics capabilities through governed data layers and tools.

Drive continuous improvement of BI performance, usability and scalability.

Collaborate with the CDO and Head of Data Governance to ensure compliance with data security, privacy and protection regulations (e.g., POPIA, GDPR).

Enforce access control, data retention and audit standards.

Ensure all data processes are aligned with enterprise governance and audit requirements.

Partner with AI & Platform Engineering, Credit Risk, Data Science and Research & Analytics teams to meet data availability and performance requirements.

Act as a key contributor in architecture forums, technology steering committees and innovation sessions.

What You’ll Bring

Bachelor’s or Master’s degree in Computer Science, Information Systems or Data Engineering.

Minimum 8–10 years of experience in data engineering or analytics, with at least 5 years in a leadership capacity.

Proven leadership in managing data engineering & BI teams within a complex, data‑driven organisation.

Advanced knowledge of cloud data architectures (AWS, Azure or GCP).

Expertise in SQL, Python, Spark, Airflow, Kafka and modern data tools such as Snowflake, Databricks or StarRocks.

Strong understanding of data warehousing, lakehouse architectures and ELT/ETL patterns.

Experience in BI and visualisation tools (Power BI, Tableau, Metabase or Looker).

Excellent communication, project management and stakeholder engagement skills.

Solid understanding of data governance, metadata and data quality frameworks.

Exposure to financial services, fintech or regulated environments is highly advantageous.