Data Engineering Team Lead

IG Group is a FTSE 100 fintech operating across five continents, serving over 1.3 million customers and handling billions of dollars in transactions. The Data Engineering team supports analytics, reporting, compliance, and client-facing data products, and powers AI and machine learning use cases. The company is consolidating its data platform onto Google Cloud Platform (GCP) from a combination of GCP-native, on-premises, and legacy AWS systems.

• Hands-on experience designing and operating large-scale data pipelines on GCP, including BigQuery, Cloud Composer/Airflow, and GCS
• Proven knowledge of Apache Kafka and real-time streaming architectures
• Experience leading or technically mentoring a team of data engineers, including setting standards and conducting code and design reviews
• Strong knowledge of data quality practices such as data contracts, pipeline observability, SLA/SLO definition, and automated alerting and remediation
• Solid understanding of SQL transformation patterns and experience with modern tooling such as dbt
• Experience managing ingestion estates with third-party connectors and in-house integrations
• Clear and confident communicator comfortable working cross-functionally with non-technical stakeholders in a regulated financial services environment with structured delivery disciplines

• Lead and deliver data pipeline and platform work within the GCP consolidation programme, managing batch and real-time (Kafka) workloads
• Coach, develop, and performance-manage a squad of data engineers, fostering engineering excellence and ownership
• Drive data quality by embedding data contracts, observability, and SLA monitoring across ingestion and transformation layers in the Medallion architecture
• Act as the technical point of contact for stakeholders in Compliance, Marketing, and Data Science, translating requirements into engineering deliverables
• Maintain delivery rigor by running sprint cadences, managing commitments, escalating risks early, and reporting on pipeline SLOs, data freshness, and DORA-style engineering metrics