BUSINESS INTELLIGENCE ANALYST
IGS ENERGY · May 2023 – Present
- Design, develop, and maintain reporting and data-integration solutions using Microsoft Fabric, Power BI, and SQL, translating business requirements into automated pipelines, dashboards, and recurring reports.
- Build and maintain Fabric Dataflows Gen2 and Pipelines to ingest SharePoint files into Lakehouse and Warehouse tables; apply SQL reconciliation and stored procedures to validate and match records, replacing a fully manual process.
- Built lakehouse structures and warehouse tables using ETL, ELT with Fabric pipelines, and automated repeated manual workflows, saving 2-3 hours per week.
- Deliver Power BI dashboards and reports using DAX and Power Query, alongside SSRS paginated reports, advanced SQL, and Excel, to give business teams accurate, timely reporting on customer and payment data.
- Analyze billing systems and databases with millions of records using advanced SQL, extracting and transforming data to support customer, payment, and credit, write-off analysis.
- Develop Power BI reports for Amazon Connect agent data covering schedule adherence, break tardiness, and agent-, department-, and manager-level KPIs (average handle time, average answer time, inbound calls answered, outbound calls made), with daily, weekly, monthly, and quarterly views.
- Clean, explore and analyze customer and payment datasets using SQL and Python (Pandas, NumPy), and build predictive, risk scoring models for fraud, non-payment and disconnect risk.
- Quantify business impact in dollars through regular ad-hoc analysis: extract data from database tables with SQL, transform it using SQL and advanced Excel functions (SUMIFs, IFs, Index-match, Power query transformations etc) and summarize results in pivot tables for stakeholders.
- Implement row-level security (RLS) across the agent scorecard reports so each agent sees only their own metrics, maintaining data integrity and governance.
- Uncover opportunities to reduce bad debt by deep-diving into fraud and non-payment cases, analyzing customer behaviour, identifying the attributes, trends and gaps behind them, and recommending actions to the Enrollment and Collections teams and senior management, contributing to 20% bad debt reduction.
- Led requirements gathering and collaborated with cross-functional teams such as Commercial, Collections, BI, Sales, to build reporting dashboards on certain win-back initiatives and to understand why customers left.
- Wrote advanced SQL (temp tables, window functions, aggregations) to prepare the data, published the report in Power BI, and built a Power Automate flow that queries the Power BI dataset daily.
- Designed advanced Power Automate flows that notifies sales partners directly with a list of dropped customers eliminating a manual account-monitoring process.
