Responsibilities
- Act as a lead developer for the dbt project
- Build performant, modular, and well-tested models that turn data into a reliable "Source of Truth."
- Apply software engineering rigor to the data stack: version control (Git), documentation, unit testing, and CI/CD
- Partner with business leads across the company to identify manual processes that can be streamlined through data automation
- Bridge the gap between the warehouse and the business by "closing the loop"—ensuring data isn't just sitting in a dashboard but is actively driving operational tools and decision-making
- Leverage AI tools and LLMs to accelerate development cycles, automate documentation, and explore new ways to enhance data quality
- Act as a consultant to business leads to understand underlying business logic and propose better ways to structure data
- Translate loosely defined business requirements into robust, automated technical solutions
- Work closely with stakeholders to define data quality standards and establish feedback loops that ensure data captured in upstream systems is accurate, complete, and fit for purpose
Requirements
- 3+ years of experience in a high-growth data environment (Analytics Engineer, Data Engineer, or Technical Data Analyst)
- Deep, practical knowledge of dbt and cloud warehouses (we use BigQuery, but the experience is very transferrable)
- Expert-level SQL: You write complex, clean, and performant queries
- Python Literacy: Not a software developer, but you must be comfortable reading code, writing scripts for data manipulation, and handling API
- AI Comfort: You are an "AI-first" builder who is comfortable using AI tools (e.g., Cursor, GitHub Copilot, LLMs) to write code faster, debug, and stay ahead of the curve
- Ownership Mindset: You are comfortable working independently, managing your own roadmap, and taking accountability for the entire warehouse architecture
- Exceptional Communication: You can walk into a room with a non-technical team, understand their pain points, and explain your technical solution in plain English
Nice to Have
- Workflow Automation: Experience with tools like n8n, Zapier, or similar automation platforms
- BI/Semantic Platforms: Experience with Looker, Thoughtspot, or similar
- Industry Context: Experience in Payments, FinTech, Banking, or Cryptocurrency
- Reverse ETL: Familiarity with moving data from the warehouse back into SaaS tools
Benefits
- 30 days annual leave each year, excluding bank holidays
- 4 wellbeing days per year to prioritise your mental health
- 1 company volunteering day per year
- Strong benefits package including; Private Healthcare, Pension, Income Protection (long-term absence), Life Insurance, Menopause Policy, and an enhanced Parental Leave policy
Work Arrangement
Hybrid
Team
Structure: Cross-functional team within the Data and Analytics team
Additional Information
- Hybrid working arrangement
- Opportunity to collaborate with teams across the business
- No barriers to communication with anyone in the business
- Focus on empowerment, personal growth, and team success
- Professional development and learning opportunities
- Exponential opportunities for personal growth
