The Senior Data Transformation Engineer is responsible for transforming raw, ingested data into business-ready, well-modelled data assets that power analytics and reporting. As the most senior data-engineering role within the function, this position sets data modelling standards, owns the integrity of connected enterprise datasets.
Accountability & Responsibilities of Role:
- Design and maintain robust data models, conformed dimensions, and business entities that represent the organization's commercial, retail, and customer domains.
- Transform raw data into curated, business-ready datasets that are accurate, consistent, performant, and reusable across teams.
- Define and govern relationship mapping across systems, ensuring data from disparate sources is reliably connected into a coherent enterprise view.
- Own the customer identity data foundations, preparing the conformed datasets required for downstream identity resolution and customer intelligence.
- Develop and maintain transformation logic using DBT, orchestrated through Dagster, on Microsoft Fabric.
- Establish and enforce data modelling standards, testing, documentation, and version-control practices across the transformation layer.
- Implement data quality controls, automated testing, and validation to ensure trust in business-facing datasets.
- Partner with the Data Ingestion Engineer to define data contracts and resolve upstream data issues, and with the AI Engineer to ensure curated data meets the requirements of customer intelligence use cases.
- Lead design reviews, mentor data engineers, and contribute to the technical direction and standards of the data engineering function.
- Continuously optimize transformation pipelines for performance, cost, maintainability, and scalability.
Work Experience:
Minimum of 6+ years of experience in data engineering, including substantial experience in data modelling and building business-ready data assets at enterprise scale.
Experience with following is a must have:
- Advanced SQL and strong data modelling expertise (dimensional and entity modelling).
- Hands-on experience with DBT for transformation and testing.
- Orchestration using Dagster (or comparable orchestration frameworks).
- Microsoft Fabric or equivalent modern lakehouse / data warehouse platforms.
- Designing conformed dimensions, business entities, and cross-system relationships.
- Building data foundations to support customer identity and analytics use cases.
Additional good to haves:
- Experience within the Microsoft Azure data ecosystem.
- Exposure to Customer Data Platform or master data management initiatives.
- Experience in retail, e-commerce, or omni-channel environments.
Education background:
Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Engineering, or a related field. A relevant postgraduate qualification is an advantage.
Key Competencies:
- Strong technical leadership and ownership of outcomes.
- Structured, top-down thinking with sound design judgement.
- Ability to balance trade-offs across performance, cost, and maintainability.
- Excellent stakeholder management and communication.
- High standards for data quality, governance, and documentation.
