Senior Data Engineer

Are you a data engineer who takes pride in building clean pipelines, fast queries, and reliable data infrastructure?

We are looking for a talented Senior Data Engineer to join our growing and highly capable Business Intelligence team at Protein Works. You will be responsible for delivering and maintaining the business-critical data that powers decision-making across marketing, trading, operations and finance. You will own the data infrastructure that has a direct, visible impact on how the business performs.

This role sits at the heart of Protein Works' AI transition. As the team grows, you will be a key enabler of that capability, building the clean, well-governed data foundations that allow AI workflows, agents and analytical tooling to deliver value. If you're excited by the shift from traditional BI to an AI-augmented data function, this is the seat to be in.

Protein Works is a high-growth DTC nutrition brand with a clear mission: help people live healthier, happier lives through the power of nutrition. Based at our brand-new PW Campus in Speke, Liverpool, this role suits someone with an unwavering passion for data, a deep appreciation of modern data engineering on Google Cloud Platform, and the drive to keep raising the bar on quality, reliability and efficiency.

What You Will Be Doing

Pipelines & Project Delivery

• Own It End-to-End: Manage and deliver BI infrastructure from initial brief and requirement gathering through to data pipeline development (APIs / ETL / ELT / SQL).
• Enable the Analysts: Work alongside your Data Analyst colleagues to help them generate impactful business insights and analysis.

Data Platform & Quality

• Guard the Stack: Take day-to-day responsibility for the maintenance and enhancement of the data quality, security and performance of our Data Lake and Warehouse.
• Drive CI/CD: Build and maintain robust CI/CD processes for data pipelines using tools such as Cloud Build and GitHub, raising the standard of how code is tested, deployed and version-controlled.
• Manage Performance & Cost: Proactively monitor pipeline health, optimise performance, and manage infrastructure costs to ensure efficient scaling.

AI Enablement

• Make Data AI-Ready: Apply strong data modelling, semantic consistency and metadata discipline so data is discoverable and usable by both humans and AI tools.
• Partner with the AI Team: Work closely with function specific AI engineers and analysts to understand their data needs and unblock them quickly.
• Bring AI Into Your Own Workflow: Use AI tooling to accelerate your own engineering, from code generation to pipeline documentation to data quality checks.

Team & Operations Share What You Know: Champion knowledge sharing and best practice across the team and wider business.

• Demonstrable data engineering experience with a data warehouse platform.
• Extremely Strong SQL skills
• Significant hands-on experience integrating APIs.
• Experience with the Google Cloud Platform/Data Stack including: BigQuery; Cloud Functions; Cloud Storage; Pub/Sub; IAM Management; Data Studio
• Strong working knowledge of Python for pipeline development, automation and tooling integration.
• Hands-on experience with Cloud Build and GitHub for version control and CI/CD.
• Highly Organised with strong verbal and written communication, able to multi-task and consistently deliver to deadlines.
• Able to influence and collaborate effectively at all levels.
• Eye for detail and high standards in everything you produce.

Desirables

• Hands-on experience with dbt, Dataform or equivalent transformation and orchestration frameworks.
• Experience with Terraform or equivalent infrastructure-as-code tooling.
• Familiarity with statistical, ML or AI concepts, and ideally with building data layers that feed RAG pipelines, agents, or LLM-based applications. Comfort with vector databases, embeddings or semantic search is a plus.
• Working knowledge of Google's marketing stack: Google Analytics, Google Tag Manager, Google Ads and Google Search Console.
• Highly numerate: analytical and comfortable in detail., * A Proactive Self-Starter: You don't wait to be told what to do; you take extreme ownership of your domain.
• A Pragmatic Problem-Solver: You have a knack for simplifying complexity and getting things done efficiently.
• Collaborative & Positive: You are a team player who can build trust quickly across all departments and levels of the business.
• AI-Curious and AI-Native: You see AI as a force multiplier, not a threat. You're already experimenting with how it changes the way data engineering gets done.