• Build and maintain dbt models that transform raw data from multiple sources into clean, tested, well-documented datasets. • Partner with data scientists, engineers, and product teams to translate ambiguous questions into durable data models rather than one-off queries. • Improve data quality across the stack by writing tests, defining expectations for critical models, and triaging issues when something looks wrong. • Document models, metrics, and lineage so engineers, analysts, and partner organizations can self-serve with confidence. • Use Python where it's the right tool — for orchestration, ad-hoc work, or transformations that don't belong in dbt. • Raise the bar on analytics engineering practices, including code review, modeling conventions, CI for data, and documentation standards. • Perform additional engineering and data duties as needed to support the broader team.

• Design and deliver production‑grade Power BI dashboards and reports across a wide range of business domains • Connect and model data from diverse sources, including SQL Server, ERP systems, industrial platforms, and cloud services • Build robust data models with clean DAX measures and optimized Power Query transformations • Conduct exploratory analysis on large datasets to surface trends, anomalies, and opportunities not yet being tracked • Partner with stakeholders to translate domain knowledge into actionable analytics requirements • Document analytics solutions and provide training to end users • Stay current with the BI ecosystem and help shape platform evolution • Support development of analytics capabilities across business units.

Role Description Co-own Formula's entire analytics function alongside our Head of BI — half builder, half investigator, in one seat. This is not a data engineer and not a product analyst; it's the rare full-stack data person who makes both possible without a team in between, on a modern stack (Snowflake, dbt, Python, Dagster, Metabase) with AI woven into daily work. Qualifications - Hands-on hybrid experience: personally written dbt models and personally run analyses that moved business decisions. - Strong SQL and dbt in a modern warehouse (Snowflake, BigQuery, Redshift, or Databricks); able to design a data layer from scratch. - Python at the level of pipelines and analytical notebooks — pandas, applied statistics, light ML where it earns its place. - Statistical literacy you can defend — A/B testing, incrementality, correlation vs. causation handled correctly. - Russian language for day-to-day work with the team. Requirements - Someone who has been the strongest data person on a small team and is ready to do it again — with a co-owner mandate, not an executor seat. - A pragmatic builder rather than a craft-obsessed engineer: ships the model that earns its keep, not the architecture diagram. - A curious investigator who walks into the room with the answer, not the dashboard — and can push back on the question if it's wrong. - Comfortable in a no-process environment: forms the request themselves, navigates ambiguity without hand-holding. - Treats AI tools as a daily multiplier, not a novelty — already builds Claude / Cursor / GPT into how the work gets done. Benefits - Inspiring Mission: Help users live longer, healthier lives through innovative products. - Impact: Directly influence company growth with minimal bureaucracy. - Attractive Compensation: Competitive salary and comprehensive benefits package. - Work-Life Balance: Flexible working hours. - Professional Development: Tuition reimbursement. - Remote Work: Fully remote, with a preference for candidates within ±2 hours of CET. - Health insurance, gym membership reimbursement, home office support.

• Design, develop, and maintain Python-based analytical applications supporting reporting, planning, and decision-making • Build and support data pipelines and workflows, including ingestion, transformation, validation, and integration • Develop and optimize SQL queries, database schemas, and data access patterns for performance and scalability • Create user-facing dashboards, visualizations, and analytical interfaces to enable data exploration and insight generation • Translate complex analytical requirements into scalable, maintainable software solutions • Implement best practices in software development, including version control, testing, and code reviews • Support application lifecycle management, including enhancements, troubleshooting, and performance optimization • Collaborate with technical teams and stakeholders to define requirements and deliver high-quality solutions • Ensure secure development practices and proper handling of data and application dependencies • Document application architecture, workflows, and operational processes