i6 Group - Analytics Engineer

Remote - United Kingdom1mo ago

Remote Mid EMEA Cloud Computing Logistics Analytics Engineer Data Engineer Python SQL dbt Snowflake Git

Requirements

• 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. • You will be a great fit for this role if in addition to the above you have the following: • Experience with Google Cloud Platform (GCP) and its data services (BigQuery). • Familiarity with Infrastructure-as-Code (Terraform). • Experience with Containerization (Docker). • Programming proficiency in Python for automation and data manipulation.

Responsibilities

• 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.