Role purpose
We are seeking an exceptional Lead Data Engineer who combines deep engineering credibility with outstanding people leadership and stakeholder management.
You will lead multi-disciplinary data engineering teams to design, build and operate secure, resilient and scalable data products and cloud-native platforms, while translating business priorities into clear engineering outcomes.
This role is accountable not only for technical quality, but also for team performance, delivery confidence, colleague development and trusted relationships across Technology, Data, Risk and business leadership.
What we’re looking for
Leadership & people management: Proven experience leading data engineering teams in a large, complex organisation; strong coaching, delegation, performance management, workforce planning and talent-development capability.
Stakeholder leadership: Demonstrable ability to influence senior stakeholders, facilitate difficult technical/business decisions and build alignment across product, architecture, security, risk, operations and delivery.
Large-scale data engineering: Extensive hands-on background designing, building and supporting distributed data systems and large-scale processing in production, with strong software-engineering fundamentals.
Cloud data platforms: Strong experience with GCP preferred, including BigQuery, Dataflow/Apache Beam, Cloud Storage, Pub/Sub and Cloud Composer; equivalent Azure/AWS depth is valuable where accompanied by strong GCP adaptability.
Data processing & streaming: Expertise in batch and real-time pipelines, Spark/PySpark and Kafka or equivalent event-streaming technologies; strong SQL and Python, with Java/Scala/Go advantageous.
Data architecture: Strong knowledge of warehouses, lakes/lakehouses, operational data stores, dimensional modelling and modern data-product/data-mesh principles.
Engineering practices: CI/CD, Git-based workflows, automated testing, Terraform/IaC, containers and Kubernetes; experience improving developer experience and engineering controls.
Non-functional excellence: Ability to design for security, privacy, resilience, recoverability, performance, scalability, observability and cloud cost efficiency.
Regulated environments: Experience delivering data capabilities in banking, financial services or another highly regulated environment, with strong awareness of governance, audit and operational risk.
Core technology landscape
Languages & processing SQL, Python, PySpark, Spark; Java / Scala / Go
GCP & cloud data BigQuery, Dataflow, Cloud Storage, Pub/Sub, Composer, Spanner; Azure/AWS exposure
Streaming & orchestration Kafka, Apache Beam, Airflow / Composer
Data platforms & modelling Data warehouse, data lake/lakehouse, Data Vault, star/snowflake models, data products
DevOps & platform Git, Jenkins/GitHub pipelines, Terraform, Docker, Kubernetes, observability tooling
Desirable
Google Cloud Professional Data Engineer, Professional Cloud Architect or equivalent cloud certification.
Databricks/Delta Lake, dbt, MDM, Data Mesh and AI/ML or GenAI data-platform experience.
Track record leading major data transformation, cloud migration or platform modernisation programmes across multiple teams.