Sai Nikhil Gummadavelli

Business Data Analyst · Data Architect

I turn complex data into trusted decisions.

Data professional specializing in analytical modeling, reporting automation, and decision-ready BI—across public-sector operations, startup ecosystems, and enterprise teams.

Explore selected work Download résumé (Word)
Abstract data network illustration

Core toolkit

SQLQlik SensePower BIPythonAzure Data FactoryAzure DevOpsTableauArcPyOracle
01 / Selected work

Five systems. Measurable change.

A selection of data initiatives drawn from real-world work across analytics engineering, reporting, and data quality.

01

FL-SOLARIS public analytics interface

Built a public-facing Qlik Sense reporting experience for conservation, facility, budget, and compliance reporting.

  • Modeling: designed OLAP-style fact and dimension structures plus reusable Qlik associative data models.
  • Data integration: unified Oracle records managed in TOAD with GIS feature data and ArcPy workflows.
  • Analytics delivery: authored set-analysis measures, documented metadata, and published reporting extracts through Azure Blob Storage and AWS S3.
Qlik SenseSet AnalysisOracle (TOAD)Azure BlobAWS S3ArcPy
48%

improvement in custom query performance, alongside a 40% reduction in manual reporting time

02

Startup analytics platform

Architected a cloud analytics foundation for a multi-team startup environment.

  • Pipeline engineering: orchestrated ETL workflows in Azure Data Factory with SQL Server as the governed source layer.
  • BI modeling: built star-schema datasets and Power BI dashboards for engagement, financial, and operational KPIs.
  • Data quality: automated SQL and Python validation checks and optimized Azure Data Lake and Blob Storage organization.
Azure Data FactorySQL ServerPower BIPythonAzure Data Lake
10+

startup teams equipped with decision-ready dashboards; data processing time cut by 50%

03

Enterprise BI reliability & forecasting

Strengthened enterprise reporting reliability for analytics used by more than 1,000 people each day.

  • Performance: tuned SQL pipelines and standardized reusable Tableau data sources to reduce processing latency.
  • Monitoring: developed Python routines for anomaly detection, data-drift checks, forecasting, and dashboard QA.
  • Integration: consolidated API, flat-file, and on-premises database inputs into unified analytical datasets.
SQLTableauPythonAPIsPredictive modeling
1,000+

daily business users supported, with data processing latency reduced by 35%

04

FL Land Records ETL pipeline

End-to-end Python ETL pipeline that ingests, validates, and loads synthetic Florida land-records data into a SQLite star schema.

  • Validation: automated quality gates for nulls, duplicate parcel IDs, acreage and geometry issues, and date ranges.
  • Transformation: cleaned and conformed parcel and facility records into fact and dimension tables.
  • Testing: pytest suite with a generated data-quality report for every load.
  • CI/CD: Azure DevOps pipeline runs the pytest suite and data-quality gates on every commit; tagged Git releases for each deployment.
PythonSQLitepytestETLData qualityAzure DevOps
View on GitHub
4

automated quality checks — nulls, duplicate parcel IDs, acreage/geometry, and date ranges — run on every load

05

Conservation Lands dashboard

Interactive Streamlit dashboard exploring synthetic Florida conservation-land acquisitions.

  • KPIs: headline cards, funding and agency breakdowns, and annual acquisition trends.
  • Mapping: Plotly tract map with filters for agency, funding source, and year.
  • Exploration: filter panel plus a full data explorer for tract-level detail.
  • Delivery: Git version control with Azure DevOps sprint tracking; repeatable release runbook for Streamlit deployments.
StreamlitPlotlyPythonPandasAzure DevOps
View on GitHub
1

dashboard covering KPIs, funding trends, agency mix, and a tract-level map

02 / Experience

From raw inputs to reliable operations.

Hands-on delivery across public sector, startups, and enterprise consulting.

Business Data Analyst

Florida Department of Environmental Protection

  • Elicit business requirements from statewide stakeholders and translate them into BRDs/FRDs, data models, and acceptance criteria for FL-SOLARIS analytics.
  • Partner with program and compliance teams to define KPIs and lead UAT for Qlik dashboard releases.
  • Manage Azure DevOps version control and CI/CD pipelines for SQL scripts, Qlik reloads, and reporting deployments; track sprints and defects in Azure Boards.
  • Run release governance in Azure DevOps — gated CI/CD pipelines with automated data-quality checks and approvals before Qlik apps and reports reach production.

Data Architect

Domi Station

  • Designed star-schema data models and Azure Data Factory pipelines that improved data reliability and refresh times by 45%.
  • Developed Power BI dashboards for 10+ startup teams, translating engagement, financial, and operational data into decision-ready KPIs.
  • Automated SQL and Python data-quality checks and optimized Azure Data Lake and Blob storage, cutting processing time by 50%.
  • Set up Azure DevOps Git repos with a trunk-based branching strategy for ETL and analytics code; pull-request reviews and tagged releases replaced ad-hoc script sharing.
  • Built Azure DevOps CI/CD pipelines that run SQL/Python data-quality tests on every commit and trigger Power BI dataset refreshes after successful deployments.
  • Defined lightweight data-governance standards — naming conventions, documentation templates, and access controls — adopted across 10+ startup data teams.

Data Analyst

Cognizant Technology Solutions

  • Engineered high-performance SQL pipelines and Tableau data sources supporting 1,000+ daily business users while reducing processing latency by 35%.
  • Built SQL and Python audit routines for anomaly detection, data drift, forecasting, and dashboard QA, increasing trust in recurring reports.
  • Integrated APIs, flat files, and on-premises databases into unified analytical datasets for BI and predictive modeling.
03 / Profile

Built for the whole data lifecycle.

Technical depth

Analytics

Power BI, Tableau, Qlik, DAX, M Query, Tableau Prep, Alteryx

Data engineering

Azure Data Factory, Azure DevOps, Azure Boards, CI/CD, SSIS, Informatica, Azure Data Lake, Blob Storage, AWS S3/RDS

Languages

SQL, Python, R, Java, JavaScript, Pandas, NumPy, ArcPy

Databases

SQL Server, PostgreSQL, MySQL, Oracle, Azure SQL, Redshift

Education & credentials

M.S. Computer ScienceFlorida State University · GPA 3.75/4.0
B.Tech in Electronics & Communication EngineeringSreenidhi Institute of Science and Technology
Project Management Professional (PMP®)Project Management Institute · 2025
Certified ScrumMaster2026

Let’s build clarity from complexity.

Open to data, analytics, BI, and data engineering opportunities across the United States.

Email Sai

sainikhil1948@gmail.com

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