Regulatory Data Analyst

Data Visualization & Dashboarding: Design, build, and maintain high-impact, interactive dashboards and visual reports in Power BI and Tableau tailored to executive and operational audiences.

Data Modeling & Analysis: Write advanced, highly optimized SQL queries, stored procedures, and scripts to extract, clean, aggregate, and model data from large relational databases and cloud enterprise warehouses.

Spend & Procurement Insights (Preferred): Analyze corporate spending patterns, vendor performance, procurement metrics, and cost-saving opportunities through dedicated Spend Analysis frameworks.

Stakeholder Management: Collaborate directly with business leaders, product teams, and functional departments to gather requirements, define KPIs, and deliver actionable insights.

Data Governance & Performance Tuning: Optimize dashboard performance, establish visualization best practices, and ensure data integrity, accuracy, and security across all reporting layers.

Mentorship & Innovation: Guide junior analysts, standardize reporting templates, and stay updated with emerging trends in BI tools and analytics.

Experience: 6+ years of hands-on experience in business intelligence, data visualization, and advanced data analysis.

Power BI & Tableau: Advanced expertise in both platforms, including complex calculations (DAX in Power BI, LOD expressions in Tableau), data blending, custom visual development, and row-level security (RLS).

Advanced SQL: Strong proficiency in SQL for querying large dataset architectures, window functions, complex joins, subqueries, and performance tuning.

Data Wrangling: Proven track record in data transformation, data modeling (Star/Snowflake schemas), and preparing datasets for BI tools.

Communication: Exceptional storytelling abilities—converting dense analytical output into clear, executive-level visual insights.

Spend Analysis Domain Knowledge: Prior experience analyzing enterprise spend, procurement data, accounts payable, tail spend, or vendor rationalization.

ETL / Pipeline Familiarity: Exposure to data integration platforms (e.g., Alteryx, SSIS, Informatica) or cloud data platforms (e.g., Snowflake, BigQuery, Databricks).

Scripting Skills: Basic to intermediate skills in Python or R for statistical analysis and advanced data preparation.