Responsibilities 

• Build and maintain dashboards and automated reports tracking key business metrics: revenue pipeline, client engagement, operational efficiency, and team performance. 
• Write complex SQL queries to extract, transform, and analyze data from multiple sources (CRM, billing systems, internal databases, third-party APIs). 
• Define and standardize KPIs across departments, ensuring consistent definitions and measurement methodologies. 
• Perform ad hoc analysis to support strategic decisions: pricing optimization, client segmentation, churn prediction, resource allocation. 
• Clean, validate, and structure messy datasets — build repeatable data pipelines where manual exports currently exist. 
• Present findings and recommendations to leadership and cross-functional teams in clear, jargon-free language with visual support. 
• Identify data quality issues and work with engineering/ops to resolve source-level problems rather than patching downstream. 
• Document data sources, transformations, and metric definitions in a central knowledge base. 

Requirements 

• Strong proficiency in SQL (window functions, CTEs, joins across multiple tables) — this is non-negotiable. 
• Advanced Excel/Google Sheets skills (pivot tables, INDEX-MATCH, data validation, conditional formatting for executive-ready outputs). 
• Experience with at least one BI/visualization tool: Tableau, Power BI, Looker, or Metabase. 
• Ability to translate business questions into data queries and data outputs into business recommendations. 
• Experience working with messy, incomplete, or inconsistent data and building processes to clean it systematically. 
• Strong communication skills — you should be able to explain a cohort analysis to a sales lead and a CFO with equal clarity. 

Nice to Have 

• Python or R for statistical analysis and automation. 
• Experience with dbt, Airflow, or similar data pipeline/transformation tools. 
• Familiarity with SaaS metrics (MRR, ARR, LTV, CAC, churn rate, NRR). 
• Exposure to A/B testing frameworks and statistical significance testing.