> Markdown version of [/jobs/ext/3612167-senior-data-engineer-snowflake](https://www.wearedevelopers.com/jobs/ext/3612167-senior-data-engineer-snowflake). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer (Snowflake) - **Company:** AND Digital - **Location:** Bristol, UK - **Experience:** Expert - **Salary:** £87,248.0 - **Contract:** Temporary contract - **Skills:** Continuous Integration, Data Architecture, Data Governance, Data Transformation, Data Systems, Data Warehousing, Python (Programming Language), Machine Learning, Role-Based Access Control, Cloud Services, Privacy Controls, Sql Optimization, Snowflake, Integration Tests, Data Lineage, Dynamic Data, Software Version Control - **Published:** October 8, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5918735259 ## About the Role * Cloud Data Platforms: Proven expertise in architecting and operating data solutions within enterprise cloud data warehouses (e.g., modern columnar cloud platforms). * Transformation & Orchestration: Advanced SQL and Python engineering skills with deep experience in modern data transformation frameworks and workflow DAG orchestrators. * Complex Data Modeling(dbt): Demonstrated track record modeling in dbt * Data Quality & Governance: Hands-on experience establishing automated unit/integration test suites, lineage tracking, and role-based privacy controls. * Agile Delivery: Experience working in fast-paced sprint cadences, breaking down complex epics into clear, shippable increments. ## Description * Pipeline & Architecture Ownership: Design and implement automated, modular transformation pipelines in a modern cloud data warehouse, following a clean, tiered physical data architecture (from raw ingestion to governed marts). * Experience with machine learning models - Needs experience with ML development life cycle within Snowflake (Martech experience highly desirable) * Governance & Security: Implement enterprise-grade data security standards, including automated quality testing, role-based access control (RBAC), and dynamic data masking for sensitive customer information (PII). * Technical Leadership: Guide engineering best practices across CI/CD, version control, and automated orchestration, collaborating closely with product managers, analytics leads, and platform architects.