Principal Data Engineer

As Principal Data Engineer, you act as the technical authority for Group Technology, shaping data platform design and governance across multiple squads. You will lead architecture decisions, drive scalable and cost-efficient data solutions, and mentor engineers while aligning with business outcomes. This role offers impact at scale, influencing platform roadmaps, standards, and cross-team initiatives in a collaborative, innovative environment.

• competitive benefits package
• hybrid working model
• opportunity to stretch and grow career
• inclusive environment
• flexible collaboration
• support for experimentation

• Define and drive data platform and engineering strategy aligned with Group Technology objectives
• Lead architecture decisions and set scalable standards for data pipelines, storage and processing
• Collaborate with Architecture and Product to shape long-term platform roadmaps
• Design and deliver production-ready data platforms and pipelines that are scalable, observable and cost-efficient
• Champion CI/CD, automated testing, DevOps, observability and platform automation
• Strengthen data quality, governance and security; promote reusable data products
• Provide technical leadership across squads and influence senior stakeholders
• Lead cross-team, multi-system initiatives spanning geographies
• Coach and mentor engineers, support hiring and onboarding, grow Data Engineering community
• Evaluate emerging technologies and drive value-delivering tooling and approaches
• Drive continuous improvement balancing quality, speed, cost and risk

• Deep expertise in distributed data processing and cloud-native data architectures
• Proven experience delivering large-scale data platforms and production pipelines
• Strong Python and SQL, with hands-on Spark, PySpark or Scala
• Experience with dbt for data modeling and transformation
• Cloud experience (GCP, Azure, AWS) and services like Databricks, BigQuery, Azure Data Factory
• DevOps/DataOps practices and tools (CI/CD, Git, Terraform, Docker, Kubernetes)
• Experience with data quality tools and testing (e.g., Great Expectations)
• Understanding of batch, streaming and event-driven architectures
• Designing resilient, scalable, observable systems with performance and cost considerations
• Leadership: setting standards, coaching, and influencing senior stakeholders
• Comfort with ambiguity, transformation, and prioritization

• Leadership and influence
• Strong problem-solving and decision-making
• Excellent communication with senior stakeholders
• Python
• SQL
• Spark/PySpark/Scala