Data Analytics Engineer (CXT)

Travelbyinvestec; Sandton; On-site; ZAR 900,000 - 1,300,000; Full time

Job summary

Investec is seeking a Data Analytics Engineer to bridge data engineering and analytics. You will convert analyst specifications into governed, reusable data products on the Group data platform, owning the semantic layer to make cross‑channel experiences measurable.

The role emphasizes building scalable, reusable data products, analytical models and Power BI assets, enabling Business Unit Analytics teams to self-serve reliable insights with certified foundations.

Qualifications

• Strong SQL and Python for data transformation and automation.
• Understanding of dimensional and semantic modelling with Kimball techniques.
• Experience with data governance, data contracts and data quality controls.
• Familiarity with CI/CD and DevOps practices in data projects.
• Bachelor/Master in CS/Engineering; Business Analytics background helpful.
• Certifications such as DP-600/DP-700 or DP-203 may be preferred.

Responsibilities

• Design, build and maintain governed data products on the Group data platform.
• Develop semantic models, star schemas and KPI logic for cross‑channel analyses.
• Ensure data quality, conformance and certification evidence.
• Build Power BI assets for descriptive to prescriptive analytics.
• Create reusable datasets, templates and onboarding materials.

Skills

SQL

Python

Dimensional modelling

Data governance

Azure DevOps

CI/CD

Education

Bachelors or Masters in Computer Science/Engineering

Business Analytics

Tools

Microsoft Fabric

Databricks

Delta

Spark/PySpark

Azure Data Factory

Synapse

ADLS Gen2

Job description

Description

Channel Data & Insights (CDI) is a core, crosscutting capability within Investec's Client Experience Technology division. CDI governs channel behavioural data across digital and assisted-servicing channels and turns that data into trusted data products, platform analytics and evidence. CDI complements Business Unit analytics by providing consistent cross-consumer comparability on shared experiences and shared-failure visibility across digital-to-assisted transitions.

Within this context, the Data Analytics Engineer is a hands‑on build role that bridges data engineering and data analytics. The role converts analyst‑owned product specifications into governed, certified and reusable data products on the Group data platform and owns the semantic modelling layer that makes shared experiences measurable and comparable across channels and business units.

The Data Analytics Engineer enables scale through reusable platform capability rather than bespoke analytics delivery. The role builds and maintains governed data products, analytical models and semantic layers that allow Business Unit Analytics teams to self‑serve trusted insight using consistent definitions, reusable patterns and certified data foundations.

Key Responsibilities

• Data Product Engineering Design, build and maintain governed data products on the Group data platform, including bronze, silver and gold layers, reusable data marts and certified analytical data products.
• Analytical & Semantic Modelling Define and maintain semantic models, star schemas, KPI logic and behavioural entities such as events, sessions, journeys, flows, steps, intents and transitions. Maintain definition consistency across channels and business units.
• Data Quality, Contracts & Conformance Implement validation checks, testing frameworks, data contracts, governance controls and certification evidence so that data product quality is demonstrated rather than assumed.
• Insight Delivery & Platform Analytics Build analytical products and Power BI assets that support descriptive, diagnostic, predictive and prescriptive analytics over governed data products.
• Enablement & Self‑Service Create reusable datasets, templates, onboarding material, usage guidance and playbooks so that consuming analytics teams can use the data products without re‑engineering the foundations.
• AI‑Enabled & Conversational Analytics Structure datasets and semantic models so that AI agents, conversational analytics and natural language querying return accurate outputs against governed business logic and definitions.
• Automation, Engineering Practice & Operations Use Python, SQL, Azure DevOps, Git and CI/CD practices to automate analytics delivery, version control solutions and support performance monitoring and optimisation of owned products.
• Partnership & Technical Leadership Partner with analyst product owners, platform engineers, Business Unit Analytics and Business Unit Digital / Technology teams to translate requirements into technically sound, reusable and governed implementations.

Qualifications, Experience and Skills

• Strong SQL and Python, including data transformation, analysis, automation and reusable component development.
• Working knowledge of Microsoft Fabric, Databricks, Delta, Spark / PySpark, Azure Data Factory, Synapse, ADLS Gen2 and event or streaming patterns.
• Dimensional modelling and semantic modelling expertise, including Kimball star schema design, data marts, Power BI semantic models and DAX.
• Practical understanding of data governance, Microsoft Purview, Unity Catalog, data contracts, data quality, testing, lineage and certification.
• Azure DevOps, Git, CI/CD and infrastructure as code exposure.
• Bachelors or Masters in Computer Science/Engineering
• Business Analytics
• Microsoft DP-600 or DP-700 certification preferred,
• DP-203, PL-300, AZ-900 or relevant Databricks certifications recognised.

Investec Culture

At Investec we look for intelligent, energetic people filled with passion, integrity and curiosity. We value individuals who in turn value our culture that is, a flexible attitude comfortable to live with ambiguity and willing to challenge the status quo. Diversity, talent and leadership are respected in pursuit of the growth of our business. People who can manage themselves and build strong relationships in order to get things done, will perform in out of the ordinary ways in our environment.