Analytical Engineer

Indsafri; South Africa; On-site; ZAR 600,000 - 800,000; Full time

Job summary

Indsafri India Private Limited is seeking an Analytical Engineer responsible for designing and delivering structured data models for enterprise analytics. This role requires proficiency in SQL and Python, as well as experience in Data Vault 2.0 and modern data platforms like Azure and Databricks.

The ideal candidate will bridge business and technical teams to ensure the solutions meet requirements while adhering to data governance standards.

Join us to transform raw data into trusted, actionable insights!

Qualifications

• Proficiency in SQL and Python for data transformation and analysis.
• Experience with modern data platforms like Azure and Databricks.
• Strong stakeholder engagement and communication skills.

Responsibilities

• Design and implement scalable data models to support analytical use cases.
• Develop and maintain ETL/ELT processes for data transformation.
• Engage with stakeholders to translate requirements into data solutions.

Skills

SQL

Python

Data Vault 2.0

Dimensional modelling

Azure

Databricks

Tools

ETL

Data governance frameworks

Job description

We are seeking an Analytical Engineer responsible for designing, developing, and delivering structured, governed, and analytics-ready data models and datasets that enable enterprise analytics, reporting, and data-driven decision-making. The successful candidate will combine strong Data Analyst and Data Modelling expertise to transform complex raw data into trusted, business-ready information assets. Working closely with business and technical stakeholders, the Analytical Engineer will translate business requirements into scalable data solutions that support strategic objectives and deliver meaningful business insights.

The ideal candidate will have experience in Data Vault 2.0, dimensional modelling, and advanced SQL for data transformation, profiling, validation, and analysis. The role focuses on the delivery of high-quality, governed datasets that support reporting, analytics, and enterprise data products. Experience with Azure, Databricks, and enterprise data environments is preferred. Strong stakeholder engagement skills are essential, as the role bridges business and technical teams to ensure data solutions meet business requirements while adhering to data governance, quality, and architectural standards.

Responsibilities

• Design and implement scalable data models (e.g., dimensional and Data Vault) to support analytical use cases.
• Ensure alignment to enterprise data architecture and modelling standards.
• Develop and maintain ETL/ELT processes to ingest, cleanse, and transform data from multiple sources into trusted datasets.
• Deliver curated, analytics-ready data layers for consumption by downstream users.
• Analytics Enablement
• Enable business intelligence, reporting, and advanced analytics use cases through high-quality data provisioning.
• Partner with data scientists and analysts to support modelling, insights, and data-driven decision‑making.
• Data Quality, Governance & Controls
• Ensure data integrity, lineage, and consistency across datasets.
• Apply governance frameworks and standards to support auditability and regulatory compliance.
• Stakeholder Collaboration
• Engage with business and technical stakeholders to translate requirements into data solutions.
• Support delivery within cross‑functional squads aligned to enterprise data products.

Required Skills & Experience

• Proficiency in SQL and Python for data transformation and analysis
• Experience with modern data platforms (e.g., Azure, Databricks, Microsoft Fabric)
• Solid understanding of data warehousing, ETL/ELT, and analytics pipelines
• Familiarity with data governance, quality frameworks, and lineage
• Proven ability to combine engineering discipline with analytical problem‑solving
• Analytical thinking and problem‑solving
• Attention to data quality and detail
• Strong stakeholder engagement and communication
• Delivery focus within agile environments
• Governance and compliance awareness

Deliverables / Success Measures

• Delivery of trusted, analytics‑ready datasets aligned to business requirements
• Implementation of scalable and reusable data models
• Adherence to enterprise data governance and quality standards
• Enablement of timely and accurate reporting and analytics outcomes
• Contribution to data product delivery within GDA squads

Preferred Qualifications

• Exposure to real‑time/streaming data processing frameworks
• Understanding of DataOps and CI/CD practices