What you'll be responsible for

• Onboard new data sources end to end — from raw ingestion to certified marts.
• Advance data quality: tests, contracts, metrics dashboards, and anomaly monitoring.
• Contribute to warehouse architecture evolution: conventions, code review, documentation, and AI-ready data contracts.
• Build and maintain dashboards for product and business teams; design metrics and bring them to self-service.
• Drive analytical work from business question to reproducible analysis with a clear decision recommendation.
• Develop models and analyses with AI assistants (Claude Code and similar) as a primary workflow, not a shortcut.
• Help build AI-native team processes: automating routine work, agentic pipelines for documentation, testing, and ad-hoc analysis.
• Act as the translator between business questions and data model design.

What you bring

Must-have

• Proven depth in data roles — analytics engineering, data analysis with an engineering orientation, or data engineering with strong analytical capability.
• Advanced SQL — you write and review complex queries comfortably and reason about performance trade-offs.
• Hands-on production experience with dbt: incremental models, snapshots, tests, macros, and project organisation.
• Experience designing DWH layers and reasoning about trade-offs: normalisation vs. marts, full rebuild vs. incremental, SCD2.
• Python for ETL tasks and data analysis.
• Experience building dashboards and metrics with the ability to drive analysis through to a decision — not just a table.
• Real, embedded experience working with AI assistants in your daily workflow — LLMs as a core tool, not an occasional experiment.
• Experience with Airflow or another orchestration platform
• Advanced stakeholder and project management: able to take a vague business problem, decompose it into a plan, and drive it to completion independently.

Nice-to-have

• SaaS product analytics background: PLG metrics, funnel analysis, cohort modelling.
• Experience with GA4, HubSpot, or Stripe data.
• Understanding of data privacy requirements: GDPR, PII masking, policy tags.
• Experience building agentic pipelines or automations on top of LLM APIs.

What we offer

• Competitive compensation, paid in USD, transparently shared before the first interview
• Fully remote work with flexible hours
• 30 paid days off annually, plus sick days as needed
• Health & well-being support
• Learning & development budget to support your professional growth
• English lessons reimbursement
• Co-working space reimbursement
• Company-provided equipment (conditions may vary depending on the role)
• A high level of autonomy and ownership in your work
• The opportunity to make a real impact in a fast-growing global SaaS company
• A collaborative, international team with a strong product mindset