Analytics Engineer · Seattle, WA

Sanket
Thakre

5+ years at Amazon building scalable data pipelines, dimensional models, and self-serve analytics platforms on AWS — at scale.

↓ Resume LinkedIn ↗ GitHub ↗
Data Ecosystem
Scroll
0
Years at Amazon
0
Projects Built
0
Technologies
0
Users Served
About

Background

Sanket Thakre
Sanket Thakre
Analytics Engineer · Seattle, WA

I'm an Analytics Engineer with 5 years at Amazon in Seattle, designing and building scalable data pipelines, dimensional models, and self-serve analytics platforms used by business, finance, and ops teams.

My focus is the full AE stack — from ingestion and transformation (ETL/ELT, dbt, Airflow) to data modeling and the BI layer — with a strong emphasis on reliability, automation, and making data genuinely accessible.

I hold an MS in Computer Engineering from Cal State Fullerton and a BE in Electronics & Telecommunications from the University of Pune. I enjoy working at the intersection of engineering and business — building data systems that actually get used.

📍
Location
Seattle, WA
🏢
Current
BIE @ Amazon
🎓
Education
MS Computer Engineering, CSUF · BE E&T, Pune
✉️
Email
sanket.thakre3@gmail.com
Stack

Technical Skills

Data Engineering
ETL / ELTdbtApache AirflowFivetranAirbyteDimensional ModelingStar SchemaSCD TypesData Quality
Languages
SQL (Advanced)PythonPandasNumPyPySparkMatplotlibBash
Cloud & Warehouses
Amazon RedshiftSnowflakeDatabricksAWS S3AWS GlueMySQLPostgreSQLOracle
BI & Visualization
Amazon QuickSightTableauPower BILookerJupyter Notebook
Tools & Practices
GitGitHub ActionsAgile / ScrumData ContractsPerformance TuningRoot Cause Analysis
Experience

Where I've Worked

Amazon
Aug 2021 – Present · Seattle, WA
Business Intelligence Engineer
  • Designed and implemented scalable ETL/ELT data pipelines using SQL and Python to ingest, transform, and load large datasets into Amazon Redshift, serving as the data backbone for multiple product and operations teams.
  • Developed and maintained dimensional data models — star/snowflake schemas and SCD types — to standardize KPI definitions across cross-functional teams and enable reliable self-serve analytics.
  • Automated end-to-end data collection, transformation, and delivery workflows, enabling real-time insight delivery through dashboards, deep-dive tools, and alerting systems.
  • Built and maintained production dashboards and metrics tooling in Amazon QuickSight, providing real-time operational visibility to business, finance, and operations stakeholders.
  • Partnered with data scientists, software development engineers, product managers, and business analysts to gather requirements, define data contracts, and ship end-to-end analytics solutions.
  • Performed complex deep-dive analysis on key business metrics to identify root causes, surface actionable insights, and drive high-impact strategic decisions.
Projects

What I'm Building

Python dbt Airflow ✓
01
End-to-End ELT Pipeline — dbt & Airflow
PythondbtAirflowPostgreSQLGitHub Actions
NYC TLC trip data → Python ingestion → PostgreSQL → dbt dimensional mart (staging → intermediate → facts/dims) → Airflow orchestration → dbt tests + CI.
View on GitHub →
Raw Table PostgreSQL GE Checks Null · Schema · Stats Alert ✓ Pass
02
Automated Data Quality & Monitoring Framework
PythonGreat ExpectationsPandasSQL
Reusable framework for automated schema validation, null/duplicate detection, and statistical outlier alerting on Redshift/PostgreSQL tables — production-grade data observability.
View on GitHub →
Revenue Churn Cohort
03
Self-Serve Analytics Platform — E-commerce
SQLdbtRedshiftQuickSight
Raw e-commerce data → dbt dimensional model → governed metric layer → interactive dashboard (revenue, churn, cohort retention) with documented data lineage.
View on GitHub →
Contact

Let's work together.

I build reliable data pipelines, dimensional models, and analytics platforms that help teams make better decisions. If you're working on something interesting in the data space, let's talk.

✉ Email Me LinkedIn GitHub ↓ Resume