Jitin Pranav Kolathur
Data Analyst • San Francisco, CA • j********@gmail.com • +12******170 • drivetube.ai/•••••
Professional Summary
Data Analyst with 3 years of experience applying SQL, Python, and BI tools to transform data into actionable insights. Skilled in data wrangling, ETL on cloud platforms, dashboarding with Tableau and Power BI, and using statistical analysis and A/B testing to inform business decisions. Background building production ETL pipelines and evaluation frameworks for retrieval systems, seeking an analyst role to convert data into measurable business impact.
Technical Skills
Programming Languages: Python,Scala,JavaScript
Web Technologies: HTML,CSS,REST APIs
Databases: SQL
Cloud and DevOps: Azure Data Lake,Azure Data Factory,Vector Databases
Data and Analytics: Tableau,Power BI,Excel,Statistical Analysis,A,B Testing,KPI Design,Forecasting,Apache Spark,Azure Databricks,ETL,ELT Pipelines,Data Warehousing,Data Modeling,Delta Lake,Batch Processing,Data Quality,Machine Learning,Deep Learning,LLMs,Retrieval-Augmented Generation,Prompt Engineering,Embeddings,Model Evaluation,Feature Engineering
Tools and Methodologies: Git,Databricks,Data Validation & Reconciliation,Data Quality Checks,Monitoring and Alerting,Documentation and Runbooks
Skills: PySpark
Work Experience
Salesforce
Seattle, WA
Generative AI Engineer (Industry Capstone)
Mar 2026 – Jun 2026
Capstone engagement with Salesforce building enterprise AI integrations and retrieval systems to enhance knowledge discovery for CRM and agent platforms.
Tech Stack: Salesforce Agentforce, LangChain, Embeddings, Vector Databases, OpenAI API, Gemini API, Python, Prompt Engineering
- Designed and deployed production-grade RAG pipelines on Salesforce Agentforce, integrating embedding models, vector search indexing, metadata tagging, and query rewriting; achieved 91% Hit@K on an adversarial enterprise question set.
- Engineered document ingestion and chunking pipelines for heterogeneous enterprise documents, implementing metadata-driven chunk filtering and improving retrieval precision on long-context and multi-document queries.
- Optimized chunking strategies and metadata filters to reduce irrelevant chunk retrieval by ~25%, improving overall answer relevance for multi-hop and partial queries.
- Established an automated evaluation framework using RAGAS and LLM-as-a-Judge across 200+ test scenarios, cutting hallucination rates by ~30% and providing repeatable metrics for model/retriever comparison.
- Partnered with engineering stakeholders to iteratively refine prompt engineering, retriever configurations, and embedding choices, accelerating production readiness by two sprint cycles.
- Prepared integration documentation and handoff artifacts for Agentforce deployment, including retriever configuration guidelines and evaluation reports to support maintenance and future tuning.
Capgemini Technology Services India Limited
Bengaluru, India
Senior Data Engineer
Aug 2022 – May 2025
Delivered enterprise data engineering for Supply Chain and Finance domains, building ETL pipelines and data models to support analytics and executive reporting.
Tech Stack: Azure Databricks, PySpark, SQL, Scala, Delta Lake, Azure Data Lake, Azure Data Factory, Git, Tableau, Power BI
- Built and owned 12+ production-grade batch ETL pipelines on Azure Databricks using PySpark, SQL, and Scala, ingesting 3–5 TB daily across Supply Chain and Finance domains and reducing end-to-end runtime by 15% via partition pruning and join tuning.
- Modeled SCD Type 2 dimension tables and multi-layered fact schemas aligned to business KPIs, powering 8+ executive dashboards across 5+ cross-functional teams and reducing ad-hoc data resolution time by ~35%.
- Enforced data integrity across pipeline runs processing 50M+ records by deploying automated data quality checks and threshold alerting, which reduced production incident turnaround by ~40% and increased SLA compliance to 99%+.
- Diagnosed and resolved 50+ high-priority production incidents including schema mismatches, ingestion failures, and transformation logic errors, restoring data accuracy within business SLA windows.
- Implemented data validation and reconciliation workflows and collaborated with analytics teams to ensure downstream reports matched source systems, improving trust in BI outputs and accelerating decision cycles.
- Recognized with the 'Shining Star' Award for delivering measurable improvements in pipeline reliability, data accuracy, and analytics enablement across a team of 20+ engineers.
Projects
ConvoAI – Gemini-Powered Multi-Turn Agent with Persistent Chat History
Tools Used: Google Gemini, Streamlit, LLM API integration, Prompt Chaining, Python
- Architected a production-style conversational AI system using Google Gemini 2.5 Flash and Streamlit, delivering stateful multi-turn dialogue with persistent session management and scalable front-end rendering.
- Engineered advanced prompt chaining and Gemini session API logic to preserve coherent context across multi-step interactions, optimizing token efficiency and reducing context degradation over extended conversations.
Education
University of Washington, Michael G. Foster School of Business
Master of Science, Business Analytics (STEM) • Seattle, WA • Jun 2025 – Jun 2026
NMIMS SVKM’s Mukesh Patel School of Technology, Management and Engineering
Bachelor of Technology, Electronics and Telecommunication • Mumbai, India • Jun 2018 – May 2022
Achievements
- Shining Star Award — Capgemini: Awarded for delivering measurable improvements in pipeline reliability, data accuracy, and analytics enablement; recognized across a team of 20+ engineers.
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