• Projektowanie, implementacja i utrzymanie procesów ETL (SQL, PySpark) w środowisku Databricks zgodnie ze standardami zespołu. • Integracja danych z różnych źródeł: systemów Customer Care, API, plików oraz baz danych. • Automatyzacja procesów przetwarzania i ładowania danych w celu redukcji błędów i eliminacji prac manualnych. • Zapewnienie jakości, spójności oraz dostępności danych zgodnie z polityką organizacyjną. • Monitorowanie pipeline’ów oraz reagowanie na błędy, incydenty i problemy jakościowe. • Optymalizacja zapytań, transformacji i struktur danych pod kątem wydajności. • Tworzenie tabel, widoków i struktur danych wykorzystywanych przez zespoły analityczne oraz wizualizacyjne. • Współpraca z zespołami Customer Care, Data Engineering, Data Visualization oraz Process Automation przy dostarczaniu danych do raportów i automatyzacji. • Analiza semantyki danych biznesowych Customer Care i jej wpływu na KPI oraz raportowanie. • Czynny/proaktywny udział w projektach data‑driven i inicjatywach optymalizacyjnych. • Dokumentowanie procesów ETL oraz kluczowych zależności między danymi. • Stosowanie standardów jakości danych oraz najlepszych praktyk ETL.

• Lead and grow a team of analytics engineers (currently 2, scaling to 4 this year), building a culture of craft, documentation, and user empathy • Drive the rollout and adoption of Lightdash as our single source of truth for business reporting, based on a unified KPI framework currently in progress • Own all dashboard development initially - from executive reporting to operational views, with support from analysts - then fully transition the ownership to analysts as self-service matures, building the templates and processes that enable this shift • Partner with stakeholders to translate reporting needs into well-designed, maintainable data products • Design and deliver training and enablement programs for business users across all functions • Own and evolve our core dbt models and semantic layer to support key analytical use cases: customer LTV, acquisition effectiveness, retention, funnel performance, and financial reporting • Establish governance and standards: metric definitions, dashboard design patterns, modeling practices, testing frameworks, and documentation • Partner with analysts to translate their needs into scalable data assets, and with Data Engineering on pipeline reliability and data quality • Partner with Data Engineering on pipeline reliability, data quality, and infrastructure decisions • Balance rigour with delivery speed-we're still building foundations while the business moves fast

• Design and maintain the canonical revenue and subscription data model, centered on Stripe • Model subscription lifecycles including upgrades, downgrades, renewals, cancellations, refunds, and disputes • Implement ARR, MRR, NRR, churn, and customer/account counts as tested, versioned dbt models • Partner with Finance to translate business definitions into precise, production-grade SQL logic • Build and maintain reconciliation logic between dbt models, Stripe, and Finance-owned reports • Investigate and resolve discrepancies surfaced during reconciliation and downstream use • Own the technical correctness of revenue numbers used in executive and external reporting • Own data quality for all revenue and financial models, including test coverage and issue investigation • Ensure revenue models adhere to Analytics Engineering standards for documentation, lineage, ownership, and catalog synchronization • Participate in governed change workflows for critical revenue assets, ensuring changes are reviewed, traceable, and auditable • Apply sound engineering judgment when balancing correctness, reliability, and delivery speed • Establish a durable revenue and KPI foundation in the near term • As the foundation stabilizes, improve performance, maintainability, and usability of revenue models • Over time, support forecasting, cohort analysis, and advanced revenue analytics, and contribute revenue-domain expertise to broader Analytics Engineering initiatives • Work closely with Finance as the technical owner of revenue modeling • Coordinate with Data Engineering on ingestion, backfills, and schema changes across Stripe and other revenue-related source systems • Support BI and Analytics teams to ensure revenue models are usable and performant

• Own the input layer to our data platform • Evolve our core data models to support emerging use cases • Automate data flows, and layer in monitoring, alerting, and data quality tests to ensure data is accurate and up-to-date • Work collaboratively across the entire Vanna team to design and implement core clinical, operational, financial, and product KPIs • Build out our reporting and dashboarding layer in support of self-service analytics • Conduct ad-hoc analyses for internal stakeholders and clients, and present findings in a way that drives maximum impact