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Harsha Nekkalapu

Senior Business Intelligence Engineer • Dallas, Texas • h******************@gmail.com • 234****899 • linkedin.com/••••• • drivetube.ai/•••••

Professional Summary

Senior Business Intelligence Engineer with 5+ years of experience building analytics, ELT pipelines, and semantic layers for data center operations, financial services, and retail loyalty. Strong hands-on expertise in SQL and Python, designing BigQuery dimensional models and Databricks workflows, and delivering executive reporting with Looker and Power BI. Delivered enterprise consolidation projects and large-scale datasets (50M+ records) for executive dashboards and a $10B+ portfolio, and improved NL-to-SQL accuracy to 90%+ for an internal LLM interface. Proficient in developing automated data-quality checks, uplift and regression models for credit and pricing use cases, and operationalized production ELT and reporting pipelines. Comfortable driving cross-functional alignment, standardizing KPIs, and owning end-to-end analytics delivery for senior stakeholders.

Technical Skills

Programming Language: Python,R,SQL,SAS
Databases: MySQL
Data Engineering & Processing: Azure Databricks
Data Analysis & Visualization: Pandas,Power BI,Looker,PLX,Alteryx,DAX,Power Query,Regression,A,B testing
Machine Learning & AI: Uplift Modeling
AI/ML Frameworks & Libraries: scikit-learn
MLOps: Feature Engineering
ERP & Supply Chain Systems: SAP
Project Management & Collaboration: Trix,NL-to-SQL
Enterprise Platforms: IBM Maximo
Logistics & Warehouse Operations: Warehouse Management

Work Experience

Google
Remote, USA
Senior Business Intelligence Engineer – Data Center Intelligence Team
Sep 2025 – Present
Built analytics and reporting for Google's global data center operations to support capacity planning, utilization analysis, and spare-parts management.
Tech Stack: BigQuery, Looker, PLX, Python, SQL, IBM Maximo, SAP, Google WMS, Trix, NL-to-SQL
  • Partnered with data center stakeholders to define KPIs, standardize business requirements, and translate analytics needs into scalable Looker dashboards.
  • Designed modular dimensional models and a reusable BigQuery semantic metrics layer to centralize KPI definitions and reduce reporting divergence.
  • Built and optimized BigQuery ELT pipelines ingesting IBM Maximo, SAP, Google WMS, and Trix to reconcile multi-source operational datasets for downstream reporting.
  • Led a cross-functional consolidation to merge 25+ Looker/PLX dashboards into 8 enterprise dashboards, preserving functionality while reducing dashboard sprawl.
  • Developed Python-based automated data-quality checks for schema, completeness, reconciliation, and KPI validation to improve refresh reliability.
  • Analyzed compute and GPU utilization, rack capacity, and power metrics using SQL and Python to identify utilization anomalies and inform resource planning.
  • Integrated validated dashboard datasets into an internal NL-to-SQL platform and led iterative tests that increased NL-to-SQL accuracy to 90%+.
Santander Consumer USA
Dallas, TX
Senior Analyst – Credit & Fraud Risk – Auto Lease Division
Apr 2024 – Aug 2025
Provided credit and fraud analytics for a $10B+ auto lease portfolio to inform underwriting, pricing, and compliance decisions.
Tech Stack: Power BI, Power Query, DAX, Python, SQL, SQL Server
  • Built executive Power BI dashboards for a $10B+ auto lease portfolio to surface credit, residual, delinquency, and charge-off trends for senior leadership.
  • Designed and evaluated credit policy experiments using A/B testing and Python to measure uplift and advise Risk and Pricing teams on underwriting adjustments.
  • Migrated legacy Excel reporting to Power BI using Power Query and implemented optimized DAX measures to scale reporting across 50M+ records.
  • Modeled star-schema production datasets on SQL Server to improve query performance and governance for Credit and Residual Risk reporting.
  • Investigated fraud spikes through root-cause analysis of FICO, DTI, LTV, dealer, and regional patterns and issued corrective underwriting recommendations.
  • Automated end-to-end fraud and portfolio reporting pipelines using SQL and Python, reducing report generation time by ~40% while improving audit readiness.
  • Enhanced a vehicle residual-value prediction model using Python and scikit-learn for regression and feature engineering to improve forecast accuracy.
7-Eleven
Dallas, TX
Data Analyst – Loyalty Analytics Team
Jan 2023 – Dec 2023
Supported 7-Rewards loyalty analytics, performing campaign measurement and store-level segmentation to improve retention and promotions.
Tech Stack: Databricks, SQL, Alteryx, Python
  • Conducted A/B testing to measure causal impact of loyalty campaigns on sales and retention, delivering ~4% uplift.
  • Streamlined BI reporting by implementing Databricks SQL pipelines to automate recurring store-level reports.
  • Automated ETL workflows with Alteryx to reduce manual processing and accelerate KPI distribution to operations.
  • Performed exploratory data analysis on transaction data using Python and SQL to identify high-performing stores and promotion levers.
  • Built customer segmentation features in SQL to drive targeted retention campaigns and prioritize marketing spend.
  • Maintained and improved 6+ strategic reports and 10 KPI dashboards distributed via scheduled Databricks jobs.
JPMorgan Chase
Analyst – Counterparty Credit Risk
Jan 2021 – Dec 2021
Analyzed counterparty exposures for security financing transactions, producing valuation and exposure analytics to support risk teams.
Tech Stack: Python, pandas, SAS, SQL
  • Assessed security financing transactions and calculated mark-to-market exposures to evaluate counterparty credit risk across repos and securities lending.
  • Conducted trade- and counterparty-level investigations to identify drivers of daily exposure and settlement risk using SQL-based analyses.
  • Derived peak exposure using haircut adjustments and wrong-way risk heuristics as a pragmatic alternative to full Monte Carlo simulation.
  • Automated data preparation and trend analysis with Python and pandas to standardize risk inputs for daily reporting.
  • Implemented data validation and anomaly detection routines that reduced data errors by 15%.
  • Processed and reconciled legacy risk datasets using SAS to support historical exposure analysis and reporting.
Deutsche Bank
Market Risk Analyst Intern – Market Risk Analysis & Control
Aug 2020 – Dec 2020
Contributed to market risk modeling and validation, building scenario analysis and VaR back-testing for pension portfolios.
Tech Stack: Python, R
  • Built a what-if scenario analyzer to calculate trade impacts on Value-at-Risk and other market risk metrics.
  • Calculated and back-tested VaR using historical simulation full revaluation for US, UK, and German pension portfolios.
  • Developed a Python module for contributory VaR to analyze portfolio-level risk contributions.
  • Performed statistical back-tests using R to validate VaR model stability and assumptions.
  • Automated back-testing workflows with Python to compare predicted versus realized losses and streamline reporting.
  • Presented model assumptions and validation results to control teams to support governance and model acceptance.
Dunzo
Data/BI Analyst Intern – Growth Team
May 2020 – Aug 2020
Supported growth analytics by building dashboards, reports, and a normalized database schema to measure campaign and feature impact.
Tech Stack: SQL, MySQL, Python
  • Analyzed marketing campaign and feature impact on revenue using SQL and Python to maintain 6+ strategic reports.
  • Built and maintained 10 KPI dashboards to track growth, sales, and costs for product and marketing stakeholders.
  • Designed a normalized 15-table database schema in MySQL to support efficient retrieval and evolving business needs.
  • Implemented ETL scripts and scheduled jobs to populate dashboards and ensure timely report refreshes.
  • Optimized SQL queries to reduce dashboard query time and improve end-user experience.
  • Partnered with product and marketing to convert business questions into measurable metrics and reporting requirements.
Loyalty Analytics

Education

The University of Texas at Dallas
MS, Business Analytics • Richardson, TX
Coursework: Econometrics & Time Series, Prescriptive Analytics, Data Visualization, Data Warehousing & Database Management, Machine Learning, Statistics, Fintech, Advanced Analytics
Birla Institute of Technology and Science, Pilani
MSc, Economics (Minor: Finance) • Pilani, India
Birla Institute of Technology and Science, Pilani
Bachelor of Engineering • Pilani, India

Certifications

Alteryx Designer Core Certification — Alteryx
Tableau Training for Data Science — Tableau
MySQL for Data Analytics and BI — Udemy
Google Advanced Data Analytics Certificate — Coursera
Google Business Intelligence — Coursera

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