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Pranav Chandra Mothukuri

Data Engineer • m****************@gmail.com • +16******205

Professional Summary

Data Engineer with 4 years of hands-on experience designing, building and operating scalable ETL/ELT pipelines and analytic datasets on AWS and Azure. Proven work across healthcare and financial domains delivering automated ingestion, PySpark transformations, data quality frameworks and dimensional models to enable reporting, compliance and ML initiatives. Strong Python/SQL skills, experience with Glue, ADF, Redshift and Power BI/Tableau, and a track record of improving dataset accuracy and pipeline reliability.

Technical Skills

Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy
Databases: SQL,SQL Server,PostgreSQL,MySQL,Azure SQL Database
Cloud and DevOps: AWS,AWS Glue,Amazon S3,AWS Lambda,Amazon Athena,Amazon Redshift,CloudWatch,IAM,EC2,Microsoft Azure,Azure Data Factory,Azure Data Lake Storage,Azure Databricks,Azure DevOps,CI,CD
Data and Analytics: ETL,ELT Development,Data Pipelines,Data Warehousing,Data Lakes,Data Modeling,Dimensional Modeling,Metadata Management,Data Integration,Data Quality,Apache Spark,PySpark,Spark SQL,Hadoop,Hive,Kafka,Power BI,Tableau,Dashboard Development,KPI Reporting,Microsoft Excel
Tools and Methodologies: Git,GitHub,Agile Scrum,SDLC,Requirement Analysis,Cross-Functional Collaboration
AI and ML and NLP: OpenAI APIs,Prompt Engineering,NLP Data Preparation,Feature Engineering,ML data pipelines

Work Experience

HCA Healthcare
Data Engineer
May 2025 – Present
Tech Stack: AWS Glue, Python, PySpark, AWS Lambda, Amazon S3, Amazon Athena, Amazon Redshift, CloudWatch, IAM, Power BI
  • Designed and implemented AWS Glue ETL pipelines using Python and PySpark to ingest enterprise healthcare data into centralized analytics platforms, enabling trusted reporting and analytics consumption.
  • Built automated ingestion workflows with AWS Lambda and S3 to eliminate manual steps, reducing manual processing effort by 30% and improving data availability for reporting teams.
  • Implemented automated data quality validations using Python and SQL across ingestion pipelines, improving dataset accuracy by 25% and ensuring consistency for compliance and operational analytics.
  • Developed a metadata-driven ingestion framework to standardize onboarding of new data sources, improving maintainability and accelerating source integration for analytics consumers.
  • Prepared feature-ready and curated datasets to support machine learning initiatives and internal AI/LLM experiments, enabling predictive analytics inputs and knowledge retrieval capabilities.
  • Monitored Glue jobs and CloudWatch alerts, performed root-cause analysis and remediation to maintain pipeline reliability (~90%) and enforce IAM-based security controls and encryption standards.
PNC Financial Services
Data Engineer
August 2023 – July 2024
Tech Stack: Azure Data Factory, Azure Databricks, PySpark, Python, T-SQL, SQL Server, Power BI
  • Designed and maintained ETL/ELT pipelines with Azure Data Factory to integrate APIs, relational and flat-file sources into centralized financial reporting and analytics platforms.
  • Built PySpark transformations in Azure Databricks to cleanse, standardize and enrich large financial datasets, improving data quality by 25% for downstream reporting.
  • Automated ETL workflows and SQL-based transformations to reduce manual intervention by 30%, accelerating reporting readiness for finance and risk teams.
  • Created dimensional data models and curated datasets to support financial performance analysis and regulatory reporting requirements for enterprise BI.
  • Optimized complex SQL queries and transformation logic for large transactional tables, improving processing efficiency and supporting scalable data operations.
  • Developed and optimized SQL Server stored procedures, views and database objects using T-SQL to support enterprise reporting and analytics delivery.
Chewy
Data Analyst
October 2021 – July 2023
Tech Stack: SQL, Python, Pandas, Power BI, Tableau, Excel
  • Analyzed customer behavior, sales trends and operational performance using SQL and Python (Pandas) to generate actionable insights for merchandising and operations teams.
  • Designed and delivered Power BI dashboards and automated reporting solutions, improving visibility into sales performance and inventory operations by 25%.
  • Built reusable reporting datasets integrating operational and transactional systems, enabling consistent analytics and streamlining reporting processes for stakeholders.
  • Authored complex SQL queries and ad hoc analyses to support trend analysis and executive reporting, delivering timely insights for operational and strategic decisions.
  • Performed data quality assessments and validation activities, improving reporting consistency by 20% and strengthening stakeholder confidence in analytics outputs.
  • Supported ML and AI-enabled analytics proofs-of-concept by preparing historical datasets, feature-ready outputs and NLP-based text analysis for customer segmentation initiatives.

Education

University of South Florida
Master of Science, Computer Science
Kakatiya Institute of Technology and Science
Bachelor of Science, Computer Science

Certifications

AWS Solution Architect
Cloud Computing Foundation Gold Certification — Wipro
Artificial Intelligence Foundation Gold Certification — Wipro
Cloud Infrastructure (AWS) Certification — Brain O Vision
NPTEL Certification – Data Analytics with Python — NPTEL
NPTEL Certification – Robotics and Control — NPTEL
Data Science and Cloud Solutions Internship Certificate — 3CORTEX

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