Available for opportunities
Hi, I'm Ria Singh.
I turn data into clear decisions.
Data tells stories, but only if you know how to listen. I'm a data analyst who bridges the gap between raw numbers and boardroom decisions; building everything from bronze-to-gold data warehouses to executive dashboards that actually get used. My recent project identified a R$4.6M retention opportunity and validated a retention strategy
achieving 186% ROI.
I specialize in the messy middle; taking fragmented data, questionable quality, and unclear business questions, then delivering clear insights, tested recommendations, and measurable impact. After 1.5 years in market research and business analysis at IMARC Services, I'm bringing that business acumen into technical analytics; combining research methodology with SQL, Python, and Power BI.
Work
Projects
Real-world problems solved through data, from marketplace analysis to cloud ETL pipelines.
SQLPYTHONA/B TESTINGPOWER BI
OLIST Marketplace Analysis
- A 97% one-time buyer retention crisis was hiding inside 99K orders on the Brazilian OLIST marketplace. I built a full medallion data warehouse (Bronze/Silver/Gold) in MySQL and segmented 93K customers into five RFM behavioral groups.
- Then I designed a statistically rigorous A/B test validating a coupon intervention at p<0.001. Outcome: Identified R$4.6M retention opportunity and validated a retention strategy
achieving 186% ROI.
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EXCELPOWER QUERYVBAPOWER BI
FMCG Sales Automation
- A regional FMCG distributor was spending 80-115 minutes daily on manual sales consolidation and reporting—a workflow bottleneck that delayed business decisions and created error risk.
- I built a fully automated pipeline where Python generates daily CSVs at 6:30AM via Windows Task Scheduler, Power Query ingests and cleans all files automatically, and Power BI serves a single-page executive dashboard. Result: 30-40 hours/month of manual work eliminated with zero daily intervention needed.
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AWS S3GLUEATHENAQUICKSIGHT
AWS Healthcare ETL Pipeline
- Raw hospital encounter data (~100K records) was inconsistent and unqueryable for operational decisions. I built a layered cloud ETL pipeline on AWS with S3 raw/cleaned/curated zones and Glue DataBrew for profiling and cleaning.
- Glue PySpark jobs handled transformations and aggregations, Athena supported SQL validation, and QuickSight delivered stakeholder dashboards. The pipeline surfaced actionable KPIs on patient volume by department, average length of stay, and admission type distribution.
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Writing
Quick explainers on modern analytics tools, platform updates, and practical implementation takeaways.
Python · Analysis
Pandas 3.0: Faster and Smarter Analysis
Practical changes in Pandas 3.0, highlighting speed, memory efficiency, and cleaner workflows for everyday analysis tasks.
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Power BI · Reporting
Power BI Updates 2025–2026
Key Power BI updates with examples focused on what analysts can immediately apply in reporting and dashboard workflows.
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