About Me

Hi, I’m Sankarsh Nellutla

Data Engineer and Software Developer with a strong foundation in building scalable ETL pipelines, backend systems, and machine learning workflows. Based in Orlando, FL, I specialize in transforming raw data into reliable, actionable insights through clean engineering, automation, and data-driven design. My expertise spans PySpark, Apache Airflow, AWS cloud services, and machine learning frameworks like XGBoost, enabling me to deliver efficient, high-impact solutions.

My recent work includes engineering data pipelines that process tens of gigabytes daily while improving pipeline reliability and cutting runtimes by 40%. I’ve designed and optimized over 15 Airflow DAGs for complex batch and streaming workflows, implemented schema validation and automated alerting systems, and tuned Spark jobs for cost-effective cloud execution. At the University at Buffalo, I led research efforts on ML model tuning and interactive data visualizations that improved prediction accuracy by up to 15%.

With a solid background in Python development, SQL optimization, and cloud architecture, I bring a multidisciplinary approach that bridges software engineering and data science. I am passionate about building resilient data infrastructure and automating workflows to reduce manual effort and enable fast, reliable decision-making.

Committed to continuous learning and innovation, I actively explore real-time data processing, serverless computing, and MLOps best practices to drive business impact. Outside of technology, I enjoy problem-solving challenges, collaborative projects, and contributing to open-source communities.

Data Journey

Discovery (2022–2023)
Began my data journey as a Software Engineer Intern at Accenture, developing backend libraries and automation tools that processed over a million records daily. Optimized PostgreSQL queries and schemas to reduce database load, laying a strong foundation in data processing and backend development.

Development (2023–2024)
As a Graduate Research Assistant at the University at Buffalo, I designed and tuned machine learning models (XGBoost, Logistic Regression), performed extensive feature engineering, and built interactive dashboards with Plotly and Dash to communicate insights effectively.

Scaling (2024)
Currently, at Community Dreams Foundation, I engineer scalable ETL pipelines using PySpark, Airflow, and AWS Glue, handling 10–50 GB of client data daily. I introduced schema validation, monitoring alerts, and optimized job configurations, reducing runtimes by 40% and significantly improving pipeline reliability.

Innovation (Present & Beyond)
Exploring advanced topics like real-time data processing with PySpark Structured Streaming, serverless architectures with AWS Lambda, and integrated ML pipelines using Airflow and FastAPI to accelerate data-driven decision-making and build resilient, scalable systems.

Professional Experience

Data Engineer
Community Dreams Foundation | Orlando, Florida, United States
February 2025 – Present
- Engineered scalable ETL pipelines using PySpark, AWS Glue, and S3 to process 10–50 GB of client data daily, reducing manual reporting efforts.
- Developed 15+ Airflow DAGs for batch and streaming workflows, improving pipeline reliability and cutting deployment time.
- Implemented schema validation, row-count checks, and failure alerts, reducing production incidents and ensuring SLA compliance.
- Tuned PySpark transformations and Glue job configurations, lowering job runtimes by 40% and optimizing AWS compute costs.

Graduate Research Assistant
University at Buffalo | Buffalo, NY, United States
October 2023 – December 2024
- Designed and optimized machine learning models (XGBoost, Logistic Regression) improving prediction accuracy by 12–15%.
- Performed advanced feature engineering, including categorical encoding, to enhance model generalization.
- Conducted hyperparameter tuning using GridSearchCV and RandomizedSearchCV, delivering reproducible training pipelines.
- Created interactive dashboards with Plotly and Dash for visualizing model results and research findings.

Software Engineer Intern
Accenture | Hyderabad, India
October 2023 – December 2024
- Developed Python backend libraries and CLI tools to automate core business processes, handling over 1M records daily.
- Refactored PostgreSQL queries and database schemas, reducing query latency and lowering database load by 25%.
- Created reusable parsing and validation modules, automating workflows and saving 40% manual effort.
- Improved code quality by implementing PyTest test suites and integrating GitHub Actions CI, increasing test coverage to 80%.

Core Expertise

  • Data Engineering & Workflow Orchestration: PySpark, SQL, AWS Glue, AWS EMR, Apache Airflow, S3, Redshift
  • Software & Backend Development: Python, Flask, FastAPI, REST APIs, PostgreSQL
  • Cloud & Infrastructure: AWS (Lambda, EC2, S3, RDS), Docker, Serverless Architecture, GitHub Actions, CI/CD
  • Machine Learning & Model Deployment: XGBoost, scikit-learn, MLflow, Feature Engineering, Hyperparameter Tuning
  • Data Visualization & Insights: Plotly, Dash, pandas, Tableau, AWS Quick SIght
  • Testing & Automation: PyTest, modular scripting, CLI tools, workflow automation

Education

Masters in Data Science
University at Buffalo, NY (2023–2025)

B.Tech in Computer Science Engineering
Vellore Institute of Technology, Andhra Pradesh, India (2019–2023)

Interested in collaborating or discussing data-driven solutions? Let’s get in touch!