Outrider

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a Data Analyst with strong SQL and Python skills to build, optimize, and support Streamlit dashboards and the data systems behind them. This role focuses on delivering reliable, performant analytics products by developing dashboards, improving underlying tables and pipelines, and ensuring data freshness and accuracy. - Build and maintain Streamlit dashboards for business reporting and operational insights. - Optimize dashboard performance (query tuning, caching strategies, efficient data loading, and rendering). - Design, develop, and maintain SQL tables, views, and models that feed dashboards. - Implement and maintain Python-based data workflows to transform, validate, and deliver data for analytics use cases. - Monitor and improve data quality, freshness, and completeness (including automated checks and alerting). - Partner with stakeholders to define metrics, ensure consistent definitions, and translate requirements into data products. - Maintain documentation for dashboards, datasets, KPI definitions, and data lineage. - Troubleshoot data and dashboard issues, providing timely support and root-cause analysis. Qualifications - Strong experience with SQL (complex joins, window functions, performance optimization, and data modeling). - Strong experience with Python for data work (e.g., pandas, data validation patterns, and APIs as needed). - Experience building dashboards using tools such as Streamlit (ideal), Looker, Tableau, etc. - Solid understanding of analytics engineering fundamentals: dimensional modeling, metric definitions, and reproducible transformations. - Knowledge of basic software engineering practices (Git workflows, code reviews, testing, CI/CD). Requirements - Professional experience building dashboards in Streamlit, including performance tuning and maintainability. - Familiarity with modern data stack concepts (ELT patterns, semantic layers, governance). - Experience implementing automated data quality tests and observability. - Experience working with large datasets and improving query and pipeline efficiency. What Success Looks Like - Dashboards are fast, stable, and trusted by stakeholders. - Underlying tables and transformations are efficient, well-documented, and easy to maintain. - Data quality issues are detected early, with clear ownership and resolution paths. - Metrics are consistent across dashboards and align with agreed business definitions. Skills & Attributes - Strong analytical thinking and attention to detail. - Ability to balance delivery speed with data correctness and maintainability. - Clear written and verbal communication with technical and non-technical partners. - Comfortable owning end-to-end analytics products from requirements to production support. Assignment - 4-month contract with possibilities to extend.