Senior Data Analyst

About the role:

As a fast-growing business, data is the connective tissue of everything we do - from understanding how customers discover us to deciding on which new features should be rolled out. We're looking for a Data Analyst who doesn't just build dashboards, but who asks the harder questions: why are users doing this, and what should we do about it?

You'll sit at the intersection of engineering and commercial decision-making - trusted by leadership to deliver reliable data and clear narratives, and trusted by the team to keep the pipelines humming. This is an early stage opportunity for an analyst who wants to build. If that sounds like the perfect mix for you, we’d love to chat!

What you will do: 

• Drive business performance, through proactive data analysis, sharp reporting, and actionable insights that land with leadership
• Own and evolve our data pipelines, ensuring data is fresh, accurate, and trustworthy when it matters
• Build and maintain dbt models, that serve recurring business needs across Growth, Finance, and Product
• Be the storyteller in the room, presenting findings and translating complexity into clear narratives that move decisions forward
• Keep a close eye on pipeline health, troubleshoot issues proactively, and champion continuous improvement in data quality

What you will need: 

• You communicate data clearly to non-technical stakeholders - including C-level
• Strong SQL skills and working knowledge of dbt (models, tests, documentation)
• Comfortable writing Python scripts for data transformation or pipeline tooling
• Strong intuition around subscription lifecycle & paid acquisition metrics - you've lived in this space before
• Intellectually curious: You aren’t satisfied with “what” happened; you are focused on “why” it happened.
• An ownership mindset - you communicate proactively, take end-to-end responsibility, and thrive in a small, collaborative team

Nice to have: 

• Experience managing ingestion pipelines or working with orchestration tools like Airflow
• Familiarity with data quality or observability tools
• Experience using AI tools to accelerate data work - LLMs for SQL generation, documentation, anomaly detection, or pipeline debugging