Data engineer and analyst with 3+ years of experience designing and building the pipelines, data models, and warehouses that turn messy, multi-source data into reliable, analytics-ready datasets. I build ETL/ELT pipelines in Python and SQL, model data for analytics with layered and star-schema designs, and build data quality checks into every stage, working across AWS, Azure, Snowflake, PostgreSQL, and Redshift. AWS Certified Data Engineer – Associate. I pair strong engineering fundamentals with business understanding, and I apply LLMs and AI where they automate real work, so the data I deliver is trusted, well-documented, and ready for BI, analytics, and machine learning.
KR
Hi, I'm
Keval Rakholiya
Data engineer building scalable ETL/ELT pipelines, cloud data warehouses, and analytics-ready data models with Python, SQL, Spark, Airflow, and dbt on AWS and Azure.
Career
0+
Years of Experience→
Portfolio
0+
Data Projects→
Competitions
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Hackathons→
Credentials
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Certifications→
00 — Intro
About Me
01 — Career
Work Experience
Where I've worked and the impact I've made.
Present
Jun 2026 – PresentFull-Time
Own the data platform behind Century Group's recruiting and operations reporting. Designed, built, and maintain 12+ ETL/ELT pipelines in Python and SQL on Microsoft Azure that ingest data from 6+ source systems into PostgreSQL, and built layered SQL data models (raw → cleaned → reporting) that consolidate candidate, job order, placement, and recruiter activity data into 25+ standardized analytical tables, creating a single source of truth for Power BI reporting. Implemented automated data quality checks (schema validation, null and duplicate detection, referential integrity, row-count reconciliation) across 30+ tables so upstream issues are caught before they reach reports. Engineered LLM-powered document-processing workflows (OpenAI, Anthropic Claude) that extract 20+ structured fields from resumes and unstructured documents and validate them against source records before loading into analytical tables.
Key Impact
↑ Replaced 4–6 hrs/week of manual work with automated jobs that run in under 10 minutes↑ Consolidated 3 siloed reporting workflows into one source of truth↑ Improved data completeness and accuracy by ~35%↑ Cut recurring ad-hoc reporting requests by ~50% with 8+ Power BI dashboards↑ 90%+ field-level accuracy on LLM document extraction
Tools & Tech
PythonSQLPostgreSQLAzureETL/ELTData ModelingData QualityPower BIREST APIsGitOpenAIClaude
Nov 2025 – Jun 2026Contract
Led the migration of fragmented Excel/CSV reporting into a centralized SQL data warehouse, designing star-schema dimensional models with sales and web-activity fact tables and region, dealer, campaign, and date dimensions. Built ETL pipelines integrating Adobe Analytics, GA4, CRM, and vehicle sales data through APIs and scheduled extracts, standardizing schemas and join keys across sources to enable unified customer journey and attribution analysis. Automated executive and regional performance reporting with Python and SQL, wrote optimized SQL (CTEs, window functions) for funnel and cohort analysis, established validation checks between source systems and warehouse tables, and maintained a shared data dictionary of KPI logic and metric definitions.
Key Impact
↑ Improved data accuracy by 30% and cut manual reporting effort by 60%↑ Improved attribution accuracy by 35% with unified multi-source data↑ Increased executive analytics adoption by 40% across 5+ regions↑ Contributed to a 12–18% lift in campaign conversion rates
Tools & Tech
SQLPythonSQL ServerData WarehousingDimensional ModelingETLREST APIsAdobe AnalyticsGA4Power BITableau
Nov 2023 – Oct 2025Full-Time
Designed and maintained ETL workflows in Talend and Apache NiFi to ingest marketing and transactional data into Amazon Redshift and PostgreSQL. Built a data quality and validation framework in Python and SQL covering outlier detection, deduplication, and source-to-target reconciliation. Developed and optimized Redshift SQL queries and views for customer segmentation and RFM analysis, built Tableau and Power BI dashboards for customer lifecycle and campaign performance monitoring, and automated recurring reporting workflows and data refreshes to replace manual extracts. Gathered requirements from marketing, product, and analytics stakeholders and documented data sources and transformation logic.
Key Impact
↑ Improved data consistency by 50% and cut processing time by 30%↑ Reduced reporting errors by 35% with automated validation↑ Improved marketing ROI by 15% through RFM segmentation↑ A/B test insights lifted engagement 25% and retention 10%
Tools & Tech
TalendApache NiFiAmazon RedshiftPostgreSQLAWS S3PythonSQLTableauPower BI
California State University, Long Beach
Research Assistant — Data Analysis (Healthcare)
📍 Long Beach, CA
Mar 2022 – Dec 2023Part-Time
Built reproducible data pipelines in Python (pandas, NumPy) to standardize, clean, and validate clinical and survey datasets across multiple concurrent health science studies. Applied inferential statistics, including multivariate regression, ANOVA, chi-square tests, and survival analysis, to 10,000+ patient records, built statistical models in R and SPSS, and delivered analysis-ready datasets and publication-quality visualizations to faculty researchers.
Key Impact
↑ Processed 10,000+ patient records with 99% accuracy↑ Reduced data cleaning time by 40% via automated pipelines↑ Supported 3 peer-reviewed research publications↑ Built reusable datasets adopted by 5+ research teams
Tools & Tech
PythonpandasRSPSSData CleaningStatistical Analysis
Professional Career Start
02 — Learning
Education
Degrees, programs, and certifications.
03 — Stack
Skills
My full tech stack across data, AI, and engineering.
Data Engineering & Cloud
Python
SQL
Apache SparkPySpark
Apache Airflowdbt
SnowflakeAWSRedshiftAzurePostgreSQLSQL Server
BigQuery
TalendApache NiFi
Docker
GitData Science & Analytics
Python
Pandas
NumPy
Scikit-learn
JupyterMatplotlibSeaborn
RSciPySPSSMachine Learning & AI
Python
TensorFlow
PyTorchXGBoostLightGBM
MLflowAWS SageMaker
Hugging FaceSHAPFeature EngineeringGenerative AI & LLM
Python
LangChainOpenAI APIRAG PipelinesPrompt EngineeringPineconeChromaDBVector EmbeddingsFine-TuningBI & Analytics Tools
Power BITableau
Google AnalyticsAdobe AnalyticsExcel / VBASQL Server
LookerDAXSSIS
Google AnalyticsAdobe AnalyticsExcel / VBASQL ServerLookerDAXSSIS04 — Portfolio
Projects
End-to-end data projects — ETL pipelines, data modeling, BI dashboards, and machine learning.
05 — Builds
I Build at Hackathons
I have attended 7+ hackathons across California — from Stanford to San Diego — building data-driven solutions in 24–48 hours alongside some of the most driven people in tech.
LA Hacks 2025
📍 Los Angeles, California
Benchmarked six machine learning classifiers — XGBoost, Random Forest, SVM, Logistic Regression, KNN, and a shallow Neural Network — on a large gaming behavior dataset to predict player churn and session drop-off. Engineered features from raw event logs including session frequency, in-game purchase history, level progression rate, and social interactions. Handled severe class imbalance using SMOTE oversampling and class-weighted loss functions. Evaluated all models on AUC-ROC, precision-recall curves, and F1-score across 5-fold stratified cross-validation. XGBoost outperformed all baselines by 11% on AUC and was selected as the final model. Wrapped findings into an interactive Streamlit report with feature importance charts and player risk segmentation by cohort.
Hacktech 2025
📍 Pasadena, California
Built a heart disease risk prediction system using clinical tabular data from the UCI Heart Disease dataset. Performed comprehensive EDA including correlation heatmaps, distribution plots, and chi-square feature selection tests. Identified chest pain type, max heart rate, ST depression, and number of major vessels as the top predictive signals. Trained and compared Logistic Regression, Decision Tree, and Random Forest models across multiple hyperparameter configurations. The final stacked ensemble achieved 89% accuracy and 0.91 AUC on the holdout test set. Applied SHAP values to generate model explainability reports, making predictions interpretable for a non-technical medical audience. Packaged the final output as a physician-facing risk summary PDF with per-patient risk scores and contributing factor breakdowns.
HackDavis 2025
📍 Davis, California
Built a generative AI assistant for community health workers using LangChain, OpenAI embeddings, and a retrieval-augmented generation (RAG) pipeline grounded in CDC datasets, California Department of Public Health reports, and county-level health surveys. Designed a vector store using ChromaDB to index and retrieve relevant health statistics based on semantic similarity. Users could ask plain-English questions about disease prevalence, food insecurity rates, vaccination coverage, and healthcare access — and receive accurate, cited, data-backed summaries. Implemented prompt chaining to handle multi-turn conversations and context retention. Focused on accessibility for non-technical public health staff in underserved regions, with a clean Streamlit front-end requiring no technical knowledge to operate. Won recognition in the Social Good track.
TreeHacks 2025
📍 Stanford, California
Developed a real-time exercise activity recognition system at the intersection of computer vision and machine learning. Used MediaPipe Pose to extract 33 skeletal landmarks per video frame and OpenCV for live webcam frame capture and preprocessing. Engineered angular joint features from landmark coordinates — including elbow, knee, hip, and shoulder angles — to represent motion patterns rather than raw positions. Trained a lightweight LSTM network on a labeled video dataset of 8 common exercise types, achieving 93% classification accuracy on held-out clips. Added a repetition counter using angle-threshold state machines per exercise type. Demoed a live webcam interface that classifies exercise type and counts reps in real time, presented at the Stanford Healthcare Innovation track showcase to strong audience reception.
SoCal Tech Week 2024
📍 Los Angeles, California
Built a civic data intelligence platform to surface community concerns in underserved Los Angeles neighborhoods using social media and public records. Scraped thousands of posts from Reddit (r/LosAngeles, neighborhood subreddits) and Twitter/X using their APIs, targeting discussions around housing affordability, rent increases, transit access, and public safety. Preprocessed and cleaned text data with spaCy — removing noise, normalizing slang, and extracting named entities for neighborhood tagging. Applied VADER sentiment analysis to score posts at both neighborhood and topic level, then aggregated trends over time. Visualized findings in an interactive Streamlit dashboard featuring choropleth heatmaps by zip code, time-series sentiment trends, and keyword frequency breakdowns. Placed in the top 5 teams in the Social Impact track out of 80+ submissions.
UC Berkeley AI Hackathon 2024
📍 Berkeley, California
Designed and built an end-to-end automotive sales intelligence platform during a 24-hour AI-focused hackathon at UC Berkeley. Architected SSIS ETL pipelines to ingest raw dealership data from CSV exports, CRM APIs, and inventory management systems into a centralized SQL Server staging area. Applied star schema dimensional modeling with fact tables for sales transactions and dimension tables for vehicles, dealerships, regions, and time. Built interactive Power BI dashboards tracking monthly revenue by region, sales velocity by model, customer lifetime value, and inventory turnover rate. Incorporated an AI-assisted anomaly detection module using Python's scikit-learn to flag unusual pricing and discount patterns. The solution reduced manual reporting time by an estimated 70% and surfaced pricing inefficiencies across three vehicle segments, earning the Best Data Engineering award at the event.
DataHacks 2024
📍 San Diego, California
Predicted telecom customer churn by following the full CRISP-DM data science lifecycle — from business understanding through model deployment planning. Explored a real-world telecom dataset covering contract type, monthly charges, tenure, service bundle usage, and support call frequency. Performed targeted EDA to surface churn patterns by segment and applied label encoding and scaling for categorical and numeric features. Trained and compared Decision Tree, Naive Bayes, and KNN classifiers across stratified train/test splits, running grid search hyperparameter optimization for each. Surfaced that contract type and customer tenure were the dominant churn drivers, followed by whether the customer had tech support enabled. Achieved a final model F1-score of 0.84 on the holdout set. Delivered both a technical Jupyter Notebook and a non-technical retention strategy memo with targeted recommendations for reducing churn in high-risk customer segments.
Get in touch
Let's Connect
I love talking about data engineering, pipelines, and modern data platforms. Whether you have an opportunity, a question, or just want to say hi, my inbox is open.