Own the Transformation Layer: Design, build, and maintain complex dbt models to power internal BI and external customer analytics
Pipeline Management: Manage and monitor data ingestion pipelines to ensure high availability and low latency
Performance Tuning: Optimise cloud data warehouse costs and query performance (clustering, partitioning) for sub-second response times
Data Quality & Testing: Build and maintain automated testing frameworks (dbt test, Great Expectations) to proactively catch data issues
DataOps: Maintain CI/CD pipelines (GitHub Actions) for data deployment, applying software engineering principles to data workflows
Collaboration: Partner with Data Analysts to provide clean models and work with the Data Engineering Lead on architectural infrastructure decisions
Technical Documentation: Document data models, macros, and transformation logic clearly to ensure team scalability

3-4+ years of experience in Data or Analytics Engineering
Expert SQL: Ability to write complex window functions, optimise joins, and debug query plans
dbt Expertise: Deep hands-on experience in production (snapshots, incremental models, custom generic tests, Jinja/Macros)
Cloud Data Warehousing: Deep understanding of BigQuery or Snowflake, specifically clustering and partitioning strategies
Version Control: Expert-level comfort with Git, branching strategies, and Pull Request workflows
Orchestration: Experience with Airflow, Dagster, or similar tools
NoSQL Knowledge: Understanding of NoSQL structures and how to transform them into relational models
Data Contracts: Understanding of data contracts and their role in pipeline stability

Analytics Engineer

Key skills