80 → 2hours
Automated a critical validation process
Built a Python and Alteryx workflow that cut manual effort by roughly 98% while strengthening audit readiness.
Senior data analytics professional · New York
I turn complex
data into clear
decisions.
I’m Tanvi, an analytics leader with 7+ years of experience building enterprise BI, automation, and data solutions that create measurable business impact.
SQL · Python · Snowflake · Power BI · Tableau · Alteryx
7+years across consulting, healthcare, retail, and finance
30+enterprise data sources unified into one trusted platform
6M+records analyzed annually in enterprise engagements
A little about me
Business-minded.
Technically fluent.
I work at the intersection of data, technology, and business strategy. My job is not simply to build a dashboard—it’s to understand the decision behind it and make the path forward visible.
I’ve partnered with executives, finance leaders, operators, and technical teams to turn complex data into trusted systems, sharper questions, and measurable results.
MS, Information SystemsStevens Institute of Technology · GPA 3.7
Selected work
Impact, in numbers.
A few examples of how I’ve used analytics, automation, and business context to create meaningful outcomes.
8% → 31%margin
Found where the business should focus next
Revenue pointed to one product. Adding profitability, customer behavior, and channel performance revealed a stronger growth opportunity.
8M+records
Made complex data decision-ready
Delivered reporting and KPI frameworks that helped hospital leaders accelerate decisions by up to 85%.
How I turn data into decisions
Automate·Uncover·Enable
I start with the business question,
then use data to get to the decision.
Process automation · Opportunity analysis · Decision intelligence
01 · Process automation
Automated a critical validation process
Business questionWhy are we spending so much time doing this manually?
01Problem→
02Workflow→
03Result
01Before
CSVXLSDBSYS
Manual data preparationManual reconciliationManual validationManual output preparation
~80hours every cycle
workflowSQL · Python · Alteryx
StandardizeCombineReconcileValidateQuality checksFlag exceptions
Produce trusted output03After
Automated validationExceptions surfacedHuman reviews exceptions onlyTrusted output
~2hours
~98% less manual effort
02 · Opportunity analysis
Found where the business should focus next
From sales data to a more complete picture of where growth could actually create value.
Business questionWhich product should we invest in?
01Obvious answer→
02Add context→
03Better answer
The biggest number is not always the biggest opportunity.
$4.8M revenue18 products3 channels2,400 customers
01Revenue
Product A$1.2M
Product B$850K
Product C$620K
Product D$470K
Revenue says: invest in Product A.
02Add context
Sales+Costs+Customer behavior+Sales channel
The biggest number is not always the biggest opportunity.
03Compare
04Discover
Product BDirectStrongest marginHigh repeatMarketplaceModerate marginModerate repeatWholesaleLower marginLower repeat
Illustrative analysis using synthetic data.
03 · Decision intelligence
Made complex data decision-ready
Business questionWhat does leadership actually need to know?
01Complexity→
02Structure→
03Decision
8M+Records
→12Trusted KPIs
→4Attention areas
→1Executive view
An executive does not need 8 million rows. They need a trusted view of where to focus.
Before Fragmented sources · inconsistent definitions · difficult comparisonAfter One model · trusted KPIs · one executive view
01Data sources
OPSFINCRM
Multiple operational sources02SQL + data model
FACTKPIDIM
Clean · joined · structured03KPI layer
VolumeRateTrendOpportunity
One trusted definitionExecutive overviewIllustrative dashboard · synthetic data
Q4Total volume24.8K↑ 8.4%
Performance94.2%↑ 2.1 pts
YoY change+12.6%on target
Opportunity$1.2Mmodeled
Performance trend
JanSep
Category mix
A B C
Actual vs target
94.2%Target 92%
Clear decision supportI turn millions of rows into something leadership can actually use.8M+ records → 12 trusted KPIs → 4 attention areas → 1 executive view
Dashboard values are illustrative and use synthetic data.
Experience
Built across industries.
From global consulting to healthcare operations, each role has strengthened how I connect technical execution with business outcomes.
012022 — Present
Senior Analytics Consultant
Protiviti · New YorkLeading enterprise analytics engagements, executive KPI strategy, SQL data modeling, BI delivery, and high-impact automation across millions of records.
022021 — 2022
Analytics Consultant
DataDrive · MinneapolisConsolidated 30+ data sources into Snowflake and delivered scalable pipelines and Tableau analytics for sales, pricing, and CPG teams.
032020 — 2021
Business Intelligence Engineer
Holy Name Medical Center · New JerseyBuilt executive dashboards, predictive models, and operational automation supporting hospital leadership across 8M+ records.
042019
Business Intelligence Analyst
CGS · New YorkCreated retail BI solutions for 13 clients, contributing to stronger sales, inventory turnover, and product availability.
052017 — 2018
Data Analyst
Accenture · MumbaiDelivered enterprise ETL enhancements and optimized SQL processing performance by 30% for financial systems.
Capabilities
What I bring
to the table.
Analytics & BI
Power BI · Tableau · KPI strategy · Executive reporting
Data engineering
SQL · Snowflake · Data modeling · ETL / ELT · APIs
Automation
Python · Alteryx · Workflow design · Quality controls
Leadership
Stakeholder management · Requirements · Mentoring · Storytelling
Want to go deeper?
See how I work
with data.
The Analytics Lab is an interactive space to see how I assess messy data, connect multiple sources, determine what can reliably be analyzed, and surface patterns worth investigating.
Explore the Analytics Lab →Trust → Connect → Understand → Investigate
- 01Data healthCan I trust it?
- 02Data connectionsCan these sources work together?
- 03Analysis readinessWhat can I learn from it?
- 04Opportunity scanWhat deserves a closer look?
Like what you see?
Want to know more about my work, experience, or approach to analytics? Let’s connect.
Let’s talk →