Data Engineer

Data Engineer

We're looking for a Mid-Level Data Engineer to join our team and help build and evolve our data platform. You'll work across analytics engineering, data pipelines, and data quality — collaborating closely with Engineers, Data Scientists, and Product to turn raw data into reliable, scalable foundations.

Analytics Engineering & Reporting

• Build and maintain BigQuery data models using Dataform, following medallion architecture patterns (Bronze/Silver/Gold)

• Contribute to Looker dashboards and LookML models, working alongside senior engineers and analysts

• Write performant, well-structured SQL for large-scale transformations in BigQuery

• Implement data quality checks using Dataform assertions and automated alerting

• Support data observability across the warehouse — monitoring pipeline health, data freshness, and anomaly detection

Data Pipelines & Ingestion

• Build and maintain robust Python data pipelines with testing, linting, and CI/CD integration

• Work with orchestration tooling (Cloud Composer / Airflow) to schedule and monitor workflows

• Develop familiarity with CDC concepts and event-driven ingestion patterns (Datastream, Pub/Sub)

• Containerise workloads with Docker for deployment on Cloud Run or similar GCP services

Data Science Collaboration

• Support Data Scientists in moving work from notebook to production pipeline

• Contribute to feature pipelines and data preparation for ML workloads

• Help bridge the gap between research prototypes and scalable, maintainable code

• SQL proficiency — comfortable writing complex, performant queries against large datasets in BigQuery

• Dataform experience — or strong dbt experience with willingness to work in Dataform; understanding of modular, version-controlled data transformation

• Python with an engineering mindset — clean, tested, linted code; comfortable with Git and CI/CD workflows

• GCP familiarity — hands-on experience with BigQuery is essential; broader GCP exposure (Cloud Storage, Cloud Run, Pub/Sub, Datastream) is a strong advantage

• Orchestration experience — hands-on with Cloud Composer, Airflow, or a comparable tool

• Data modelling fundamentals — dimensional modelling, Kimball principles, or medallion architecture patterns

• Docker basics — able to containerise and deploy data workloads

• Collaborative and communicative — able to translate business requirements into data models and work effectively with Analytics, Product, and Data Science stakeholders

• Pragmatic approach to AI tooling — comfortable using AI-assisted development to improve productivity and code quality

• Looker / LookML experience

• Familiarity with CDC concepts and tools (Datastream, Debezium)

• Exposure to ML frameworks or MLOps tooling (scikit-learn, MLflow, Vertex AI)

• AWS experience as a complement (Redshift, Glue, RDS) — we value engineers who can draw on cross-cloud perspective

• Curiosity about sports performance data