> Markdown version of [/jobs/ext/4663968-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/4663968-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** The IG Group - **Location:** London, United Kingdom - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Business Analytics Applications, Data Analysis, BigQuery, Code Review, Continuous Integration, Data Governance, Python (Programming Language), Standard Sql, Datadog, Feature Engineering, Data Layers, Build Management, Data Lineage, Data Analytics, Real Time Data, Apache Kafka, Video Streaming, Looker Analytics, Data Pipelines - **Published:** November 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=24793bef656ce17f ## About the Role * 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 under operational conditions * Familiarity with modern transformation tooling, particularly dbt, and an appreciation of Medallion (Bronze/Silver/Gold) architecture patterns and how they support self-service analytics * A quality-first mindset: experience implementing data contracts, lineage tracking, observability tooling, and pipeline SLO monitoring in a production environment Comfortable working in a regulated financial services environment, with an understanding of data governance requirements (lineage, access control, audit trails) and the discipline to operate within change control processes * ## Description You'll join IG's global Data & Analytics team, working closely with analysts across the business to understand what they need from the data and how they use it. The models you build underpin the team's reporting, analysis and self-serve tools, so your work has a direct impact on how analytics is delivered across IG. It's a collaborative team that values quality, shares knowledge openly and is always looking for better ways of working. You'll have the space to shape our modelling standards and practices, and the support to grow your skills as the team and platform evolve. Your role in the Team's Success: Our global Data & Analytics team turns data into decisions across Commercial, Marketing, Product and Operations. We sit close to the business and own the modelled data layer that our analysts, dashboards, and AI tools rely on. As our Analytics Engineer, you'll own that layer. You'll design, build and maintain the dbt models in BigQuery that power Looker reporting and self-serve analysis - and make sure they're trusted, documented and built to scale. You'll play a central role as we evolve our warehouse into a global setup that supports every region we operate in, so the work you do will shape how analytics runs across IG. This is a hands-on role with real ownership. You'll work day to day with analysts across the team, with Central Data and Engineering on upstream sources who depend on the data you ship. What you'll do : * 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 the GCP consolidation and Medallion architecture build-out * Own data quality within your delivery area: define and implement data contracts, quality checks, and observability instrumentation so that pipeline health is visible and SLAs are met * Develop and maintain in-house connectors and third-party native integrations that form part of IG's ingestion estate, ensuring resilience, monitoring, and long-term maintainability * Contribute to engineering standards and best practices across the squad - including code review, dbt modelling patterns, CI/CD pipeline hygiene, and change control for shared datasets