Data Engineer

IG Group is a FTSE 100 fintech operating across five continents, serving over 1.4 million customers and handling billions of dollars in transactions. The Data function serves both central and divisional business lines across IG's entire estate, with a focus on consolidating data platforms onto GCP, improving data quality, and enabling self-service access to trusted data. The Data Engineering team underpins analytics, reporting, compliance, and client-facing data products, powering AI and machine-learning use cases.

• Proven hands-on experience building and operating production-grade data pipelines on GCP, including BigQuery, Cloud Composer/Airflow, GCS, and Cloud Run, with strong SQL and Python skills
• Solid experience with Apache Kafka or equivalent streaming technologies, including designing event-driven ingestion patterns and troubleshooting real-time data flows
• Familiarity with modern transformation tooling, particularly dbt, and understanding of Medallion (Bronze/Silver/Gold) architecture patterns supporting self-service analytics
• Quality-first mindset with experience implementing data contracts, lineage tracking, observability tooling, and pipeline SLO monitoring in production
• Comfortable working in regulated financial services with knowledge of data governance requirements such as lineage, access control, audit trails, and disciplined change control processes

• Design and build robust batch and real-time data pipelines on GCP, including Kafka-based event streams, BigQuery transformations, and Airflow-orchestrated workflows as part of GCP consolidation and Medallion architecture build-out
• Own data quality within delivery area by defining and implementing data contracts, quality checks, and observability instrumentation to ensure pipeline health and SLA compliance
• Develop and maintain in-house connectors and third-party native integrations ensuring resilience, monitoring, and long-term maintainability
• Contribute to engineering standards and best practices including code review, dbt modeling patterns, CI/CD pipeline hygiene, and change control for shared datasets
• Support feature engineering and model data pipelines for Data Science and AI-ready data foundations including lineage, reproducibility, and freshness guarantees for ML workloads