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Sravya Pilli

Senior Data Engineer • United States • s*****************@gmail.com • +19******221 • drivetube.ai/•••••

Professional Summary

Senior Data Engineer with 5+ years of experience designing, building, and optimizing scalable data pipelines and cloud data platforms. Proven track record with AWS and Azure ecosystems, Snowflake and Redshift data warehouses, Databricks and Spark-based ETL, and metadata-driven ingestion frameworks. Skilled at improving data quality, performance tuning, and delivering analytics-ready lakehouse architectures to support risk, fraud, and executive reporting.

Technical Skills

Programming Languages: Python,R
Frameworks and Libraries: scikit-learn
Databases: SQL,Snowflake,PostgreSQL,MongoDB
Cloud and DevOps: AWS,Azure,Amazon S3,Azure Data Lake,Azure DevOps,Docker
Data and Analytics: Amazon Redshift,Azure Synapse Analytics,Apache Spark,Hadoop,Apache Kafka,Spark Streaming,Power BI
Tools and Methodologies: Git
Skills: PySpark
Orchestration & ETL: Azure Data Factory,AWS Glue,Apache Airflow

Work Experience

Citi Bank
Senior Data Engineer
June 2025 – Present
Data engineering for banking and corporate finance — built and maintained pipelines used for transactions, credit analytics, risk, AML and fraud reporting.
Tech Stack: AWS Glue, Amazon S3, Snowflake, Databricks, PySpark, Python, AWS Lambda, Apache Airflow, Amazon Redshift, EMR, Athena, Spark SQL
  • Designed and implemented Medallion Architecture (Bronze/Silver/Gold) using AWS Glue and S3, standardizing ingestion and transformations and reducing end-to-end onboarding time by ~30%.
  • Built a metadata-driven ingestion framework with Snowflake and S3 using a centralized configuration catalog to parameterize pipelines and support ingestion from dozens of source systems, improving developer reuse.
  • Developed scalable ETL/ELT pipelines using AWS Glue and PySpark to process large Parquet transaction datasets, improving downstream reporting throughput and processing speed by ~30%.
  • Performed PySpark data quality analysis on Databricks to identify and remediate issues, increasing customer account data accuracy by 30% and supporting regulatory reporting requirements.
  • Optimized Snowflake table design with partitioning and clustering strategies for historical ledger data, improving query performance by ~50% and lowering compute cost for analytics workloads.
  • Implemented monitoring and automated alerting for overnight ETL using AWS Lambda and Python, reducing troubleshooting time by 30% and decreasing recurring pipeline errors by 25%.
Select Health
Data Engineer
Aug 2024 – June 2025
Healthcare/insurance data engineering — modernized lakehouse and analytics layers using Microsoft Fabric, Databricks, and Snowflake to support claims and executive reporting.
Tech Stack: Microsoft Fabric, Azure Data Factory, Databricks, Snowflake, PySpark, Power BI, Azure DevOps, Azure Logic Apps, Delta Lake, DAX
  • Architected a Medallion Architecture on OneLake with Databricks Spark and Snowflake to handle enterprise data streams, reducing data latency by 30% and enabling predictable downstream analytics.
  • Designed and maintained ingestion pipelines using Microsoft Fabric and Azure Data Factory to ingest high-volume payloads from REST APIs and SFTP, increasing ingestion speed by 30%.
  • Embedded automated data validation and profiling in Fabric-compatible PySpark notebooks to detect structural anomalies and schema drift before downstream consumption.
  • Led migration of legacy on-prem ingestion workflows into a Delta Lake environment within Fabric, modernizing pipelines and reducing end-to-end latency by 30%.
  • Engineered Power BI semantic models with advanced DAX and explicit relationships to produce low-latency executive dashboards using Fabric Direct Lake patterns.
  • Implemented CI/CD for notebooks and pipelines with Azure DevOps and automated orchestration using Azure Logic Apps, cutting deployment time by 40% and scheduling errors by 50%.
Cognizant
Programmer Analyst / Data Engineer
Aug 2021 – Feb 2023
IT services delivery supporting cloud modernization and analytics for enterprise clients; focused on migrating on-prem systems to Azure and stabilizing production ETL.
Tech Stack: Azure, SQL Server, Python, Pandas, Git, JIRA, JavaScript
  • Spearheaded migration of legacy data systems to Azure Cloud, cutting infrastructure operational costs by 25% while improving query processing scalability for client analytics.
  • Engineered and optimized high-performance SQL scripts and stored procedures to ETL data from disparate relational sources, improving extraction throughput and reliability.
  • Used Python and Pandas for high-speed data manipulation to prepare analytical datasets and accelerate downstream reporting and model training preparation.
  • Troubleshot production data load failures and optimized SQL Server joins, subqueries, and schemas to stabilize production ETL and reduce recurring failures.
  • Built stakeholder-facing dashboards and visualizations using JavaScript and modern web frameworks to translate warehouse metrics into actionable insights.
  • Drove Agile delivery practices including daily Scrum, sprint planning, and retrospectives to align cross-functional teams and maintain sprint commitments.
NAB
Data Analyst
Aug 2020 – July 2021
Banking analytics and data visualization work focused on improving engagement metrics and establishing data storage and processing standards.
Tech Stack: Power BI, DAX, PySpark, ADLS Gen2, SQL, Google Data Studio, Excel
  • Developed Power BI dashboards and DAX calculations to surface participation metrics, driving an 18% uplift in user engagement identified through analysis.
  • Performed data cleaning and exploratory data analysis using Excel and Google Sheets to prepare reliable datasets for reporting and stakeholder review.
  • Built an interactive Google Data Studio dashboard to visualize key metrics for management and streamline monthly reporting cycles.
  • Adopted PySpark to implement DataFrame transformations and established ADLS Gen2 storage standards to improve large dataset processing and accessibility.
  • Authored complex SQL queries to analyze participation and operational metrics, enabling data-driven prioritization of product and engagement initiatives.
  • Documented analysis processes, presented findings to senior management, and translated results into action items that improved reporting accuracy and decisions.

Education

University of Central Missouri
Master’s Degree, Computer Science • Warrensburg, MO
Velagapudi Ramakrishna Siddhartha Engineering College
Bachelor’s Degree, Information Technology • Vijayawada, India

Certifications

Microsoft Certified: Fabric Data Engineer Associate — Microsoft
Microsoft AZ-900 — Microsoft
Microsoft Certified: Azure AI Fundamentals — Microsoft
Advanced Google Analytics — Google
Introduction to Data Science — Cisco Networking Academy
Python 3 Programming — Coursera

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