Senior Data Analyst

Role Overview

Own high-impact analytics that turn complex questions into clear, decision-ready insights. You’ll lean heavily on SQL and modern BI (Metabase, Grafana) to build trustworthy dashboards, craft compelling visual stories, and standardize measurement—ideally with familiarity in pharmaceutical workflows and constraints.

What You’ll Do

• Build dashboards & reports: Design, publish, and maintain reliable views in Metabase and Grafana for real-time business and operational performance.
• Write great SQL: Develop and optimize complex queries (CTEs, window functions, performance tuning) over large datasets; document and reuse components.
• Tell stories with data: Choose effective visual techniques—time series, distributions, funnels, cohorts, heatmaps, control charts, Pareto, box/violin plots, small multiples—to communicate clearly.
• Own metrics: Define KPIs/OKRs and metric definitions; align stakeholders on a single source of truth.
• Explore & recommend: Run exploratory analyses to surface trends/anomalies and deliver practical, quantified recommendations.
• Alerting & self-serve: Implement dashboard subscriptions and threshold alerts to minimize manual monitoring.
• Partner cross-functionally: Collaborate with data engineering and business teams; contribute to data quality checks, documentation, and lineage.
• Apply statistics: Use sound hypothesis testing (A/B, t-tests, χ², non-parametric tests as needed) and clear inference to support decisions.
• (Pharma) Respect regulated data needs; support analytics for domains like quality/supply operations, pharmacovigilance/complaints, and market insights.

Minimum Qualifications

• 5+ years of hands-on analytics experience with a strong focus on SQL and BI.
• Expert SQL: advanced querying, performance tuning, and analytics-oriented data modeling.
• BI excellence: proven track record building clear, reliable dashboards in Metabase and/or Grafana; strong visualization fundamentals and UX for data.
• Statistics (must-have): hypothesis testing, experimental design/A-B testing, assumptions/diagnostics, power & sample sizing, regression basics, and handling multiple comparisons.
• Python or R (basic proficiency): Pandas/NumPy or tidyverse; familiarity with notebooks and Git.
• Product mindset: translate ambiguous questions into well-defined metrics, datasets, and dashboards; crisp written and verbal communication.
• Collaboration: ability to work cross-functionally, manage stakeholders, and deliver in a fast-paced environment.

Nice to Have

• Power BI experience.
• Pharma familiarity: understanding of regulated environments and compliance-minded data handling.
• Experience with scheduling/ELT tools (e.g., Airflow/dbt).

Core Tech Stack

• Languages: SQL, Python or R (basic proficiency)
• BI & Visualization: Metabase, Grafana (nice-to-have: Power BI)
• Data Modeling & Storage: Postgres, ClickHouse
• Data Quality & Catalog: tests in dbt or SQL