• Partner with our founding team and company leadership to build out a best-in-class analytics system to give our partners insight into their parking lot and garage performance and super charge our internal organization. • Work with our engineering, data, sales, marketing, and product teams to understand the data needs of the business and produce pipelines, data marts and other data solutions that enable better decision-making. • Develop scalable code that transforms raw data in an automated and efficient manner to ensure curated and ready-to-analyze data is always available. • Identify new data sources we can leverage to improve the company’s performance in core business activities; this can take the form of collaborating with our engineering team to improve data logging, scraping data from the web, or using third-party datasets. • Troubleshoot and communicate data issues to internal stakeholders, including by building code tests to alert us when there is a problem in our data before it affects end users. • Play a critical role in helping define data team best practices, workflows, and documentation to ensure we are building a sustainable and scalable data function.

• Think of yourself as someone who is one of the people responsible for designing, developing and implementing ETL Solutions using SSIS to integrate data for different applications. • Develop and maintain integration solutions in accordance with best practices. • Build auto generated reports and/or data extracts to support business analysis. • Update existing solutions based on requirements provided by end users. • Coordinate with business analysts to fully understand the business requirements. • Provide technical assistance to troubleshoot and resolve existing process failures. • Create/Maintain technical documentations for all developed applications.

• 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