Analytics Engineer (dbt/Data Engineering)
Summary
Analytics engineer who owns the transformation layer on Snowflake: designing modular dbt models, writing automated data-quality tests, documenting lineage, and debugging failed jobs or slow models. Day to day it's hands-on dbt/SQL engineering with code review, CI/CD, and performance/cost tuning.
Looking for an engineer who wants to own the transformation layer, not just feed it. This role is about building and maintaining the dbt models and pipelines that turn raw data into clean, trusted, production-grade datasets - with Snowflake as the platform and dbt as the daily tool of choice.
You’ll work across the full lifecycle of the transformation layer: designing modular dbt models, writing tests that catch problems before they reach anyone downstream, and diagnosing issues when something breaks - a failed job, a data-quality defect, a model running slower than it should. It’s hands‑on engineering work with real ownership, not ticket‑based maintenance.
Responsibilities will include:
- Designing, developing and maintaining modular dbt models across the data layer
- Implementing automated data-quality tests, model and column documentation, lineage and source-freshness controls
- Diagnosing and resolving failed dbt jobs, data-quality defects and model-performance issues
- Developing reusable transformation patterns and business logic that can support multiple downstream use cases
- Optimising Snowflake queries and dbt models for performance and cost efficiency
- Participating in pull requests, code review and CI/CD processes
- Contributing to standards, frameworks and best practices for analytics engineering
Looking for an engineer who wants to own the transformation layer, not just feed it. This role is about building and maintaining the dbt models and pipelines that turn raw data into clean, trusted, production‑grade datasets - with Snowflake as the platform and dbt as the daily tool of choice.
You’ll work across the full lifecycle of the transformation layer: designing modular dbt models, writing tests that catch problems before they reach anyone downstream, and diagnosing issues when something breaks - a failed job, a data-quality defect, a model running slower than it should. It’s hands‑on engineering work with real ownership, not ticket‑based maintenance.
Responsibilities will include:
- Designing, developing and maintaining modular dbt models across the data layer
- Implementing automated data-quality tests, model and column documentation, lineage and source-freshness controls
- Diagnosing and resolving failed dbt jobs, data-quality defects and model-performance issues
- Developing reusable transformation patterns and business logic that can support multiple downstream use cases
- Optimising Snowflake queries and dbt models for performance and cost efficiency
- Participating in pull requests, code review and CI/CD processes
- Contributing to standards, frameworks and best practices for analytics engineering
Skills we are looking for:
- Advanced SQL and dimensional modelling
- Extensive and hands‑on production experience with dbt (non‑negotiable, not just exposure)
- Experience with Snowflake or a comparable cloud data warehouse
- Git‑based development, code review, automated testing and CI/CD practices
- Proven ability to diagnose and resolve data pipeline and transformation issues
- Comfort working independently on defined areas of the data stack, escalating complex architectural decisions where needed
- Excellent communication & stakeholder management skills