Results-Driven Python Data Engineer with ETL Expertise

Shravya Vura

SUMMARY

Results-driven Python Developer and Data Engineer with 6+ years of experience designing scalable data pipelines, automating cloud workflows, and developing backend services that improve operational efficiency. Skilled in building end-to-end ETL/ELT solutions using Python, SQL, AWS (Lambda, Glue, Redshift, S3), and Airflow, and creating analytics dashboards that drive business decisions. Strong background in developing API integrations, automation scripts, and backend services, with proven ability to optimize data quality, performance, and reliability. Adept at translating complex requirements into technical solutions, collaborating with cross-functional teams, and delivering high-impact products in fast-paced environments. TECHNICAL SKILLS

Programming Languages: Python (Pandas, NumPy, PySpark, Scikit-learn, Requests, Boto3, FastAPI/Flask), SQL, PL/SQL Databases: Oracle, Db2, PostgreSQL, MySQL, RDS, Redshift Skills: Data modeling, ETL, Data warehousing, Data Mining, Data Visualization, Data warehousing, Agile Software Development, Relational Data Modeling

Tools and Software: Tableau, Power BI, Microsoft Excel, AWS (Lambda, S3, Glue, EMR), Docker, Jenkins, GitHub, Gitlab, PySpark, Airflow, DBvisualizer, DataStage, Apache beam, Apache, Apache Hadoop, Spark, Azure Databricks WORK EXPERIENCE

Cox Communications, GA September 2025 - Present

Python Developer

Responsibilities:

Cox is a telecom company focused on improving broadband services and network reliability for customers using data and automation.

● Worked on building Python-based backend services to integrate telecom APIs and device-level data, helping teams get better visibility into network performance and outages.

● Built ETL pipelines using Python and SQL to bring together data from multiple sources like subscriber activity, modem performance, and Wi-Fi usage, which helped in identifying network issues faster.

● Used AWS services like Lambda, S3, Glue, and Redshift to automate data ingestion and processing, reducing manual work and improving the reliability of data pipelines.

● Wrote Python scripts (using libraries like Pandas and Boto3) to automate network diagnostics and configuration checks, which helped reduce repetitive manual effort for operations teams.

● Applied basic machine learning techniques using Scikit-learn to analyze network performance data and identify patterns in outages, helping teams take a more proactive approach to issue detection.

● Used Python (Pandas, NumPy) to perform exploratory data analysis on large-scale telecom data and built simple predictive models to flag potential network anomalies.

● Enhanced dashboards by incorporating trend analysis and anomaly detection logic, helping engineering teams prioritize critical network issues faster.

● Created dashboards in Tableau and Power BI to track network KPIs and outage trends, which helped engineering teams reduce investigation time by around 30–35%.

● Improved SQL queries and data models in PostgreSQL and Redshift, which made reporting faster and more efficient for internal teams.

● Worked closely with network, DevOps, and analytics teams to understand requirements and convert them into scalable data solutions.

Vanguard, PA January 2024 - August 2025

AWS Data Engineer

Responsibilities:

Vanguard is an investment management company that relies heavily on data to support financial decisions and portfolio management.

Built data pipelines using AWS tools like EMR, S3, Glue, and Redshift to process large volumes of financial data and make it available for analytics.

Developed AWS Lambda functions using Python to move data from DynamoDB to Redshift, which made it easier for teams to access and use data for reporting.

Automated the creation and shutdown of EMR clusters using Lambda, which helped optimize resource usage and reduce unnecessary costs.

Used SQL and Python (Pandas) to analyze data and generate reports, which helped improve data retrieval time and overall reporting efficiency.

Used Python (Pandas, Scikit-learn) to perform data analysis and build basic predictive models to identify trends in financial datasets and improve reporting insights.

Conducted exploratory data analysis and feature engineering on large datasets to support data-driven decision-making for business teams.

Integrated simple data validation and anomaly detection logic within ETL pipelines to improve data quality and reduce inconsistencies.

Cleaned and optimized datasets by removing redundant fields, improving data quality and making downstream processes more efficient.

Worked on migrating existing systems to the cloud and setting up CI/CD pipelines using Jenkins, which helped streamline deployments.

Built Tableau dashboards to present business insights in a simple way, helping stakeholders make quicker decisions. Infosys Ltd, India May 2019 - October 2021

Data Engineer

● Orchestrated data extraction and transformation using SQL stored procedures, Python scripts, and automated report/dashboard generation in Power BI, delivering accurate insights and improved operational performance.

● Developed metrics and real-time dashboards using Apache Kafka to monitor the health of data streaming pipelines, ensuring continuous data quality and performance optimization.

● Created multiple test cases per interface to validate product functionality and ensure no data loss during migration processes.

● Migrated applications and data pipelines to the cloud, improving scalability and efficiency. Reduced monthly operational costs by 15% through better resource management and streamlined processes.

● Integrated robust version control with Git and established seamless CI/CD pipelines using Jenkins to enhance collaboration and deployment efficiency.

● Leveraged Snowflake to optimize queries, improving data retrieval speed by 20% and scaling large datasets to support enhanced data engineering and machine learning workflows. Infosys Ltd, India January 2019 – May 2019

Data Engineer Intern

● Analyzed Datasets comprising over a million records, utilizing Python, SQL and Machine Learning Algorithms to extract

● actionable insights that resulted in a 30% increase in the performance.

● Implemented and maintained a scalable data warehousing solution using Google Cloud Platform, optimizing data storage, processing, and improved query performance by 20% for efficient data analysis and reporting in a high- volume music industry environment.

● Conducted preliminary data analysis and contributed to the preparation of data visualization reports using Power BI EDUCATION

Northeastern University, Boston, Massachusetts December 2023 College of Engineering

Master of Science, Data Analytical Engineering

Jawaharlal Nehru Technological University, Hyderabad, Telangana May 2019 Bachelor of Technology, Computer Science and Engineering.

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