Data Engineer, Kitman Labs

• 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

Desirable

• 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
• You'll work on a modern GCP-native stack — BigQuery, Dataform, Looker, Cloud Composer, Cloud Run — with room to grow into platform ownership, CDC pipelines, and MLOps as your experience develops. We care about clean code, good data, and pragmatic engineering over perfection