I'm a Senior Data Analyst at Google, based in the Bay Area, CA. I specialise in turning complex, large-scale data into clear insights and compelling narratives — from partner-facing analytics for the Android ecosystem to AI-powered self-service reporting tools used across Google's hardware and software divisions.
Over 10+ years I've worked at the intersection of data engineering, statistical analysis, and visual storytelling across tech, finance, and retail — at Google, Robinhood, State Street, and Ahold USA. I'm driven by the moment a well-designed chart changes a business decision.
I leverage Gemini CLI and Code Assist daily to accelerate development cycles, while maintaining a strong foundation in SQL, Python, and data visualisation built through a decade of production work.
// career by industry
Google SQL · Snowflake · Oracle · BigQuery
Tableau · Looker · Spotfire · Power BI
Gemini CLI · Code Assist · AI Agents · MCP Servers
Pandas · NumPy · scikit-learn · Matplotlib · Seaborn
Regression · Time Series · Inferential · SPSS
ETL · Data Modeling · Executive Reporting
Core tools · 10 years of production use
Core Competency · 10+ Years
SQL is the language everything else is built on. At Google I write complex queries, pipelines, and analytical datasets across BigQuery at petabyte scale — device activations, app usage, partner metrics. At State Street I built stored procedures and triggers powering daily regulatory submissions to the Federal Reserve Board. The tool I reach for first, every time.
Visualization · 9 Years
Tableau is where analysis becomes a decision. I've designed partner-facing dashboards for Android OEM teams at Google, portfolio performance dashboards at State Street, and store-level KPI monitoring at Ahold. The goal is always the same: one chart that makes the answer obvious without a slide deck to explain it.
Programming · 8 Years
Python handles what SQL can't. I build Pandas pipelines for data cleaning and normalization, use scikit-learn for predictive models, and automate recurring reporting workflows. At State Street I built ETL automation that ran without fail for 5 years. At Google I use Python to accelerate analysis on Android telemetry at scale.
AI Tools · 2 Years · Growing Fast
Gemini CLI and Code Assist are now embedded in my daily workflow — from query optimisation to code generation to sophisticated data analysis. I launched an internal AI-powered reporting portal at Google enabling self-service SQL generation for analysts across the Android ecosystem. The skill growing fastest in my toolkit.
Data Science · 7 Years
The backbone of my data science work. Exploratory data analysis, time series decomposition, statistical testing, and feature engineering all run through Pandas and NumPy. At State Street I built predictive models for portfolio risk using scikit-learn. At Google these libraries power analysis on billions of Android device data points.
Enterprise BI · 5 Years
Power BI and Looker cover the enterprise BI layer — standardised dashboards, scheduled reports, and self-service analytics for non-technical stakeholders. I've deployed Looker for financial platform monitoring at Robinhood where operational accuracy was non-negotiable, and Power BI for management reporting at State Street.
Career timeline · 2015 – present
Senior Data Analyst · Sep 2021 – Present
Mountain View, CA
Lead partner-facing analytics for Android OEMs, chipset manufacturers, and mobile carriers across 7 business verticals — device growth, app usage, activations, device health, shipments, regulatory, and licensing. Launched an AI-powered self-service reporting portal using Gemini CLI and Code Assist.
Financial Data Analyst · Jun – Sep 2021
Boston, MA
Built operational metrics dashboards to monitor financial platform health and core trading metrics. Automated daily financial summary reports and Python reconciliation pipelines supporting the Accounting team's book-close with accuracy and timeliness.
Business Intelligence Analyst · Feb 2016 – Jun 2021
Boston, MA
Designed Tableau and Spotfire dashboards for portfolio performance monitoring across a multi-billion dollar institutional asset book. Owned daily regulatory financial reporting to the Federal Reserve Board. Built ETL pipelines, predictive models with scikit-learn, and complex SQL stored procedures.
BI Developer Co-op · Jan – Jun 2015
Boston, MA · Stop & Shop
Developed enterprise dashboards for store-level KPI and performance monitoring. Designed MicroStrategy mobile dashboards with interactive functionalities for iPhone and iPad. Built automated data comparison processes using IBM SPSS for statistical modeling.
How data moves through my workflow
Ingestion Layer
Everything starts with knowing exactly where data lives and how reliably it arrives. I work with BigQuery data warehouses at Google ingesting petabyte-scale Android telemetry — device activations, app usage, partner metrics — alongside Snowflake and Oracle systems at prior roles. Owning the pipeline starts here.
Transformation Layer
SQL is the primary instrument. Complex queries, views, stored procedures, and triggers clean and model raw events into analytical datasets. At Google I architect pipelines across 7 business verticals; at State Street I built the reporting layer that fed Federal Reserve Board submissions every single day.
Analysis Layer
Python handles what SQL can't — statistical modeling, machine learning, and automation. I build Pandas pipelines for data cleaning and normalization, use NumPy and scikit-learn for predictive models, and automate recurring reporting workflows that previously required manual intervention across teams.
Acceleration Layer
Gemini CLI and Code Assist are now embedded in my daily workflow for code generation, query optimisation, and sophisticated data analysis. I launched an AI-powered internal reporting portal at Google enabling self-service SQL generation and automated data summaries — reducing analyst toil across the Android ecosystem.
Presentation Layer
Data without communication is noise. I design dashboards in Tableau, Looker, and Spotfire that translate complex analytical outputs into business decisions. From store-level KPI dashboards at Ahold to partner-facing Android OEM reporting at Google — the goal is always the same: one chart that makes the answer obvious.
Output Layer
Analysis ends at the decision, not the dashboard. I translate complex findings into executive presentations, one-pagers, and compliance reports for senior leadership, engineering, finance, marketing, and legal — across Google, Robinhood, and State Street. The insight only matters if it changes something.
6 projects · 4 companies · 10 years
Lead partner-facing analytics for Android OEMs, chipset manufacturers, and mobile carriers across 7 business verticals — device growth, app usage, activations, device health, shipments, regulatory, licensing, and enterprise. Architect and maintain data pipelines that power executive dashboards and one-pagers driving commercial decisions across the global Android supply chain.
Launched an internal self-service analytics portal using Gemini AI — natural language SQL generation and automated data summaries for unified Android ecosystem reporting. Enabled analysts and non-technical stakeholders to explore data independently without submitting ad-hoc requests, cutting analyst toil and democratising data access across Android teams at Google.
Drive cross-functional monetization analytics for Android partner and carrier payments, collaborating with Engineering, Finance, Marketing, and GTM teams to align data insights with commercial agreements. Deliver analysis that directly informs multi-million dollar partnership decisions and ensures revenue reporting accuracy across the global Android commercial ecosystem.
Designed Tableau and Spotfire dashboards enabling business teams to monitor portfolio performance and risk across a multi-billion dollar institutional asset book. Owned and delivered daily regulatory financial reporting to the Federal Reserve Board for 5 consecutive years without a single missed submission. Developed complex SQL views, stored procedures, and triggers powering the entire reporting layer.
Led data integration projects using ETL and data modeling techniques across multiple stakeholders, ensuring accuracy and reliability at every stage. Built custom Python scripts to automate data fetching, sequencing, cleaning, and normalization across production pipelines. Performed exploratory data analysis and built predictive models using Pandas, NumPy, and scikit-learn for portfolio risk assessment that ran daily for 5 years.
Built operational metrics dashboards using SQL, Python, and Looker to monitor financial platform health and core trading metrics on the Robinhood platform. Developed daily financial summary reports supporting the Accounting team's book-close process with accuracy and timeliness. Automated Python reconciliation pipelines to ensure reliability of daily financial reporting — zero errors on record.
› EXPLAIN ANALYZE
SELECT * FROM rahul_kale
WHERE role = 'Senior Data Analyst';
› _
EXPLAIN ANALYZE · total cost: 91ms
Stage 1 · 8ms
Every query begins with understanding what's being asked. I scope requirements, map data sources, and design the analytical approach before writing a single line of SQL or Python. At Google, this means aligning with Engineering, Finance, and GTM teams before a pipeline is architected.
Stage 2 · 26ms · Critical Path
The most expensive phase: a full scan of 10+ years across 4 companies and multiple industries. Finance at State Street, fintech at Robinhood, consumer retail at Ahold USA, and big tech at Google. Broad coverage means richer joins later in the plan.
Stage 3 · 18ms
Indexed expertise returns in O(log n). SQL, Python, and Tableau are the primary indexes — deeply optimised through a decade of repetition. Gemini AI is a newer index, recently added to the query planner and already accelerating development cycles at Google.
Stage 4 · 22ms · Critical Path
The core operation: cross-referencing domain knowledge (finance, mobile, retail) with the technical stack. The join condition is business impact — only combinations that produce measurable outcomes make it through. Where analytical thinking meets engineering precision.
Stage 5 · 11ms
Group by impact. 10+ years, 4 companies, 7 business verticals, petabyte-scale Android data at Google, multi-billion dollar asset books at State Street. SUM(insights_delivered) WHERE stakeholder_satisfaction = TRUE.
Stage 6 · 6ms
Results ordered by executive relevance and formatted for human consumption. Complex analysis distilled into one-pagers, dashboard tiles, and boardroom slides. The fastest phase — because the hard work was done in the scan and join. Query complete.
A practical guide to using AI tools in a data analyst's daily routine.
How I translate complex analysis into one-pagers that land with senior leadership.
What changes — and what never does — when you move industries as a data analyst.
› CONNECT WITH ME
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