name: Sri Gayathri Sahithi Morapakala
location: Seattle, WA
education: MS Data Science, 2025
current_focus: Cloud Data Engineering & Full-Stack BI
previously_at: [Deloitte, Third Estate Analytics]
certifications: [AWS Data Engineer, Microsoft Fabric, Databricks Data Engineer]
superpower: Turning messy data into pipelines that scale — and dashboards that tell the story
fun_fact: I blog about the things I get wrong first, so others don't have toI'm a Cloud Data Engineer who builds end-to-end — from raw ingestion to executive dashboards. I care about data quality, pipeline reliability, and making insights actually accessible to decision-makers. Currently seeking roles where I can architect data systems that matter.
AWS Data Engineer (DEA-C01) • Microsoft Fabric (DP-700) • Databricks Data Engineer Associate
Click the badges above to verify
| Domain | Technologies |
|---|---|
| Cloud & Infra |     |
| Data Engineering |      |
| Data Warehousing |    |
| BI & Visualization |    |
| Backend & APIs |    |
| ML & AI |    |
| DevOps & Tools |    |
|
dbt Core · Snowflake · SQL · Python · ELT · Medallion Architecture Production-grade ELT pipeline for a fintech use case — modeling customer transactions, merchant spend, and finance KPIs across raw → staging → marts layers. Built on Snowflake with dbt Core, featuring data quality tests, full lineage, and a medallion architecture designed to power personal finance dashboards. 📝 Blog: I Built a Production-Grade ELT Pipeline from Scratch — Here's Everything I Learned |
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AWS CDK · TypeScript · Azure Pipelines · S3 · Lambda Production-grade data quality framework for pharmaceutical data built entirely from scratch using Infrastructure-as-Code. Designed to validate, cleanse, and route pharma datasets through automated pipelines — no drag-and-drop tools involved. |
SQL · Data Modeling · Predictive Analytics · Python End-to-end sports data management system — from schema design to predictive modeling. Built for enhanced analysis of athlete performance with normalized data models and ML-powered forecasting. |
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Tableau · Power BI · SQL · DAX A collection of 3 interactive, stakeholder-ready dashboards:
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Regression & classification models for predicting wine quality scores |
NLP-based word clustering using embedding techniques & unsupervised learning |
Deep dive into convolutional network architectures & optimization techniques |
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Self-supervised pre-training methods for image classification — exploring representation learning without labeled data |
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Spring Boot · MongoDB · Atlas Search · Swagger · Maven Production-style microservices API with pagination, validation, full-text search via MongoDB Atlas, and Swagger docs. Designed as a reusable template for REST API development. |
📌 At Third Estate Analytics — Architected and delivered an end-to-end data pipeline
for a fast-growing analytics startup, designing ingestion workflows, applying data
quality checks, and deploying reliable cloud-based ETL processes that reduced data
defects and accelerated time-to-insight for downstream BI consumers.
📌 At Deloitte — Built and optimized data validation frameworks across production
pipelines, resolving 15+ recurring data quality issues. Implemented automated
cleansing and monitoring that improved pipeline data quality by 25%, reduced
client-reported data incidents by 35%, and achieved 99.5% system uptime —
delivered as the most junior engineer on the team.
I'm always open to collaborating on data engineering projects, BI dashboards, or discussing cloud architecture patterns. If you're building something interesting with data — let's talk.
