Data Engineer - Scaled Experimentation

• Strong Python engineering skills, including well-tested production code, API development (FastAPI or similar), and sound software design.
• Solid experience with a modern data stack: workflow orchestration (Airflow or similar), a cloud data warehouse (Snowflake preferred), and SQL you can debug and optimise under real data volumes.
• Experience running services in production, including CI/CD pipelines, containerisation, Kubernetes or gitops-style deployments, and observability.
• A data quality mindset - you notice when two numbers that should match do not, and you dig until you know why.
• Clear written communication, as RFCs, technical documentation, and async collaboration are core to how this team works.

• Familiarity with experimentation concepts: A/B testing, exposure vs. assignment, SRM, power analysis, CUPED, holdouts.
• Experience with a data catalogue or metadata platform (DataHub/Acryl or similar).
• Comfort making changes across the stack, including a TypeScript/React UI when the project calls for it.
• Experience building platforms or tooling for internal engineering customers.
• Interest in AI-assisted developer tooling (MCP servers, agent-friendly data exports).

We are on a mission to pioneer the world's next era of play. As we grow across Europe and Latin America, we're building The Playstack - the technology powering the next generation of sports, gaming, and fan experiences. Join us, and help make it the most widely used platform in the world! From operations, to marketing, to product, we are looking for talented people who will shape how millions of customers play, watch, and connect every day.

You will join the Scaled Experimentation team, who build and run Super Technologies' internal Experimentation Platform: the system every product and engineering team uses to run trustworthy A/B tests, holdout experiments, and feature flags at scale. As a Data Engineer, you will own the data backbone of this platform, with the Metric Store and evaluation data pipelines at its centre, spanning everything from Airflow DAGs and Snowflake models to FastAPI services, CI/CD, and the integrations that keep metric definitions in sync across DataHub, GitHub, and the platform UI.

• Direct, visible impact: your pipelines and services determine whether hundreds of experiments across Super Technologies produce trustworthy results.
• A genuinely cross-disciplinary team: backend, data science, and product working in one loop, with strong statistical rigour (SRM analysis, hashing audits, metric noise profiling).
• Modern tooling and real autonomy: you deploy your own services, and RFC-driven decisions mean your voice shapes the platform.

• Own and evolve the Metric Store: the metric definition repository, its FastAPI service layer, and the Git-backed, PR-based workflows that let users create and edit metrics through the platform UI.
• Build and maintain evaluation and monitoring pipelines in Airflow on Snowflake, covering experiment evaluation, SRM detection, exposure log processing, and alerting.
• Deploy services to production end to end, including Docker images, gitops-based Kubernetes deployments, GitHub Actions CI/CD, and monitoring with Prometheus and Grafana.
• Investigate data quality and trust issues, reconciling exposure counts across tables and dashboards, debugging SRM signals, and validating bucketing and hashing behaviour.
• Collaborate with backend engineers, data scientists, and the product manager, contributing RFCs and design docs and reviewing experiment setups for internal teams.
• Improve developer experience for platform users through documentation, data exports, and tooling, including MCP and AI-assisted interfaces to experiment data.