Kaustubh Dangche
Data Engineer • Chicago, IL • d**************@gmail.com • 312****708 • drivetube.ai/•••••
Professional Summary
Data Engineer with 2 years of experience building production-ready data pipelines, validation frameworks, and executive dashboards using Python and SQL. Designed ETL pipelines that processed 500K+ records monthly and reduced manual data preparation by 35%, while standardizing reporting tables for consistent cross-team KPIs. Skilled in BI and visualization with Power BI and Tableau, creating reusable measures and interactive dashboards for leadership reporting. Applied time-series forecasting (Prophet) and machine learning (scikit-learn, Pandas) to civic and healthcare datasets to improve operational planning and forecast accuracy. Experienced with geospatial analysis using GeoPandas and large dataset processing on BigQuery. Comfortable owning end-to-end workflows, enforcing data quality, and maintaining version-controlled codebases with Git to deliver automated reporting to stakeholders.
Technical Skills
Programming Language: Python,SQL
Databases: PostgreSQL,MySQL,Google BigQuery
Version Control & Development Tools: Git
Testing & QA: Quality Assurance
Data Warehousing: Data Modeling
Data Analysis & Visualization: Pandas,Power BI,Tableau,Power Query,GeoPandas
Machine Learning & AI: Time Series Forecasting
AI/ML Frameworks & Libraries: scikit-learn,Prophet
Data Integration & ETL: ETL,Data Validation
Project Management & Collaboration: Automated reporting workflows
Quality Assurance & Compliance: Root Cause Analysis
Work Experience
Tele Commuter Resources
Remote
Data Engineer
Aug 2025 – Present
Remote data engineering role building ETL and validation pipelines for public census and geospatial data to support stakeholder reporting and leadership dashboards.
Tech Stack: Python, SQL, Pandas, Git
- Designed ETL pipelines in Python and SQL to ingest and transform public Census and geospatial sources, processing 500K+ records monthly and reducing manual data preparation time by 35%.
- Implemented automated data validation checks using SQL and pandas to detect missing values, duplicates, and format issues before downstream consumption, preventing invalid records from reaching dashboards.
- Built analysis-ready SQL views and reporting tables that standardized metric definitions across teams, enabling consistent KPI reporting and reducing ad-hoc metric requests.
- Standardized repository structure and version control with Git to improve reproducibility and accelerate handoffs between analysts and engineers.
- Documented pipeline schemas, data contracts, and validation rules to shorten onboarding and reduce data questions from stakeholders.
- Optimized daily data loads by refactoring transformation steps in Python to reduce pipeline runtime and improve stability for scheduled reporting runs.
Center for Neighborhood Technology (CNT)
Chicago, IL
Urban Analytics Practicum
Jan 2025 – May 2025
Urban analytics practicum analyzing parcel-level geospatial and emissions data to inform municipal redevelopment and investment planning.
Tech Stack: Python, SQL, Tableau, GeoPandas, BigQuery
- Analyzed 5.2M+ geospatial records using Python and SQL to calculate spatial KPIs across 6,000+ urban parcels, surfacing performance trends used in redevelopment decisions.
- Built interactive Tableau dashboards consolidating parcel, emissions, and demographic layers to provide a single leadership view and cut executive report preparation time by 50%.
- Performed root-cause analysis on cross-dataset quality issues to identify schema mismatches and inconsistent geocoding, reducing error propagation into downstream models.
- Engineered spatial joins and feature engineering with GeoPandas to produce neighborhood-level metrics used in investment prioritization.
- Created reproducible data pipelines and intermediate BigQuery tables to enable repeatable analysis and reduce ad-hoc extraction effort for city stakeholders.
- Presented analytical findings and technical recommendations to municipal stakeholders, translating complex geospatial results into actionable project priorities.
Excelerate Internship
Data Analyst
June 2023 – July 2023
Marketing analytics internship focused on paid-social campaign analysis and automated performance reporting to support budget and channel decisions.
Tech Stack: Excel, SQL, Power Query
- Analyzed performance data across 10+ Facebook campaigns using SQL to identify underperforming segments and recommended budget reallocations that improved overall marketing ROI by 20%.
- Built automated Excel dashboards with Power Query and Pivot Tables to track 8 weekly KPIs, reducing manual reporting effort.
- Developed SQL extracts and cleansing routines to merge campaign, spend, and conversion data from three sources for consistent analysis.
- Automated routine reporting steps to produce refreshable executive summaries for weekly business reviews, improving cross-team visibility.
- Validated campaign attribution logic and performed statistical comparison of segments to support optimization recommendations.
- Delivered concise performance reports and actionable insights to marketing leadership to inform scaling and budget decisions.
Urban Analytics
Projects
Chicago 311 Service Request Volume Forecasting
Tools Used: Python, Prophet, Pandas, GeoPandas, Tableau, BigQuery
- Analyzed 2.1M+ 311 service request records using Pandas and BigQuery to prepare time-series datasets by service type and community area.
- Built weekly forecasting models with Prophet to predict request volumes and identified seasonal spikes linked to weather and demographics, improving baseline MAE by 18%.
- Integrated Census demographic data and a weather API as external regressors to improve model accuracy and operational planning.
- Developed a Tableau dashboard that visualized predicted vs actual volumes across 77 areas to support resource allocation and service planning.
Medical Appointment No-Show Analysis & Predictive Modeling
Tools Used: Python, Pandas, scikit-learn, Tableau
- Analyzed 110K+ electronic health records with Pandas to prepare features and imbalance-handled datasets for modeling.
- Trained and evaluated logistic regression and tree-based classifiers in scikit-learn to identify high-risk no-show patients, achieving 78% accuracy on validation.
- Built an operational Tableau dashboard to track no-show KPIs by lead time, demographics, and reminder type to guide targeted outreach.
- Recommended operational interventions based on model outputs that supported scheduling changes to reduce missed appointments and improve clinic efficiency.
Sales Insights & Reporting Dashboard
Tools Used: Power BI, DAX, Power Query, Data modeling
- Built a Power BI dashboard consolidating revenue, margin, volume, and trend data across five product regions with drill-down capability for weekly business reviews.
- Designed a maintainable data model and developed 15+ reusable DAX measures to enable refresh-ready reporting with zero manual intervention.
- Automated data ingestion and transformation steps using Power Query to reduce manual reporting time and ensure consistent metric calculations.
Education
Illinois Institute of Technology
Master of Applied Sciences (MAS) in Data Science • Chicago, IL • May 2025
Ajeenkya D.Y. Patil University
Bachelor of Technology in Information Technology, Data Science • Pune, India • May 2023
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