Venkata Manvitha Ala
Chicago,IL
Summary
8+ years of experience in data engineering, specializing in SQL database design, ETL/ELT processes, and cloud-based data solutions. Extensive experience in T-SQL for writing, debugging, and optimizing complex queries, stored procedures, and functions. Proficient in managing and maintaining Azure SQL Managed Instance, ensuring high performance, scalability, and security of cloud databases. Expertise in SQL Server Integration Services (SSIS) and Azure Data Factory (ADF) for designing and maintaining efficient data pipelines. Strong experience in data modeling (e.g., star schema, snowflake schema) to support analytics and reporting requirements. Skilled in performance tuning, query optimization, and indexing strategies for efficient data storage and processing. Proven track record in implementing ETL/ELT processes to integrate data from disparate sources and transform it into usable formats. Experience in translating business requirements into scalable data solutions with an emphasis on data integrity and data quality. Cloud expertise with hands-on experience in Azure and other cloud platforms, integrating and managing large-scale data systems. Adept at identifying performance bottlenecks and optimizing query execution and data storage to ensure cost efficiency. Collaborated with cross-functional teams to design, develop, and optimize data architectures and reporting solutions. Skilled in Agile development methodologies, participating in sprint planning, code reviews, and iterative development to ensure timely delivery. Knowledge of data governance principles and implementation of data security and compliance practices across data systems. Strong communication skills with the ability to collaborate effectively with both technical and non-technical teams. Bachelor’s degree in Computer Science, Information Systems, or a related field. Experience working with REST APIs, integrating databases with web applications and services. Hands-on experience with data integration tools, ensuring smooth data transfers and reducing processing time. Deep understanding of data privacy regulations and the ability to ensure compliance while handling sensitive data. Experienced in designing scalable database architectures to support growing data needs and high-volume workloads. Ability to troubleshoot complex data issues and implement solutions that address both technical and business needs. Highly motivated to stay up-to-date with emerging technologies and applying them to enhance data management and analytics. Expertise in business intelligence tools such as Power BI for data visualization and report generation. Excellent problem-solving and analytical skills, with a focus on enhancing data workflows and ensuring high data quality. Strong knowledge of data warehouse and data lake concepts for storing and processing large datasets.
Overview
11
11
years of professional experience
1
1
Certification
Work History
Lead Data Engineer
Bank Of America
Chicago, United States
03.2024 - Current
- Designed and implemented scalable ETL pipelines using T-SQL, SSIS, and Azure Data Factory (ADF) to integrate financial data from various systems and ensure accurate and timely reporting
- Optimized SQL queries and stored procedures, reducing query execution times by 40%, resulting in faster data processing for real-time reporting
- Migrated legacy financial systems to Azure SQL Managed Instance, enhancing scalability, performance, and security for critical financial data storage and access
- Developed and maintained data models using star schema and snowflake schema, providing optimized and efficient data structures for financial analytics
- Implemented data validation rules to ensure the accuracy and consistency of transactional data, improving reporting reliability
- Integrated financial data sources such as transaction history, account details, and market data, ensuring seamless integration and enabling a comprehensive financial reporting platform
- Collaborated with business stakeholders to translate business requirements into technical solutions, ensuring that data pipelines aligned with reporting needs and KPIs
- Designed and deployed Power BI dashboards for financial analysis, allowing senior management to access real-time, interactive reports on revenue, expenses, and financial forecasts
- Improved database performance by implementing indexing strategies and optimizing stored procedures, resulting in a 35% reduction in query response times for large financial datasets
- Led Agile sprint cycles, coordinating with cross-functional teams for requirements gathering, project planning, and iterative delivery of data engineering solutions
- Implemented security best practices in the Azure environment to ensure the protection of sensitive financial data, adhering to industry standards and compliance regulations
- Utilized Azure DevOps for version control, CI/CD pipelines, and continuous integration, streamlining deployment processes and enhancing team collaboration
- Ensured compliance with financial regulations (e.g., SOX), implementing audit logging and data tracking for all transactions, and ensuring that the data pipeline met regulatory requirements
- Developed automated alerts and monitoring systems using Azure services to proactively detect data discrepancies and performance issues, improving the operational health of financial systems
- Conducted code reviews and provided mentoring to junior engineers, ensuring high-quality coding standards and adherence to best practices in database development
- Enhanced data recovery processes, implementing regular backups and disaster recovery procedures, ensuring minimal data loss in case of system failures
- Collaborated with data scientists to integrate machine learning models into the financial data pipeline, enabling predictive analytics for credit scoring and fraud detection
- Managed database capacity by forecasting data growth and ensuring the Azure SQL Managed Instance was appropriately scaled, avoiding performance bottlenecks
- Delivered data quality reports and implemented data cleansing workflows, ensuring only clean, high-quality data was loaded into the data warehouse
- Conducted performance tuning of SQL queries and optimized database operations, resulting in a 30% increase in overall query performance
- Led initiatives to automate manual reporting processes, reducing the time spent on manual data aggregation and improving team productivity
Lead Data Engineer
Medline Industries
Chicago, United States
10.2023 - 02.2024
- Developed and optimized ETL pipelines using Azure Data Factory (ADF) to process and integrate patient data from multiple healthcare systems, ensuring data consistency and reliability for reporting and analytics
- Created and maintained complex SQL queries for data extraction, transformation, and loading (ETL), ensuring smooth and efficient data flow from legacy systems to a modern data warehouse
- Designed and implemented data models in Azure SQL Database, adhering to best practices for normalization and data structuring, to support healthcare analytics and reporting
- Implemented data validation checks to ensure data integrity in patient records and medical billing data, which were crucial for accurate reporting and compliance
- Collaborated with cross-functional teams to understand healthcare data requirements, helping translate those requirements into scalable and efficient data engineering solutions
- Worked alongside BI analysts to support reporting requirements, optimizing SQL queries to ensure high performance for large-scale healthcare datasets
- Utilized Azure DevOps for version control and CI/CD pipelines to streamline development and deployment processes across different environments
Sr. Data Engineer
CVS Pharmacy
Chicago, United States
10.2020 - 10.2023
- Developed scalable ETL pipelines using Azure Data Factory (ADF) to integrate claims, customer, and policy data from multiple sources (legacy databases, APIs, and external data providers), improving data availability for reporting and business intelligence
- Designed and implemented a robust data warehouse in Azure SQL Database, employing star schema and snowflake schema to efficiently support business intelligence and reporting needs for insurance claims and policy data
- Optimized SQL queries and stored procedures to handle high-volume insurance data, ensuring fast, reliable performance for both operational and analytical purposes
- Led the migration of historical claims data to Azure Data Lake, enabling cost-effective data storage and improving data processing and retrieval for advanced analytics and machine learning applications
- Implemented complex data transformation processes in Azure Data Factory, automating the extraction, cleaning, and transformation of policyholder data, ensuring consistency across all internal systems
- Designed and executed data validation and quality checks, ensuring that the customer and claims data met internal quality standards, reducing data discrepancies and ensuring business compliance
- Created interactive Power BI dashboards for insurance claims and policy analysis, allowing non-technical stakeholders to monitor key metrics like claim frequency, claims costs, and risk analysis
- Developed data models using T-SQL, improving data performance and minimizing redundancy for insurance claims analytics, ensuring seamless integration with reporting tools
- Collaborated closely with cross-functional teams, including business analysts and data scientists, to ensure the data warehouse aligned with business needs and advanced predictive modeling efforts
- Implemented data governance policies across the data pipelines, ensuring compliance with industry regulations (e.g., GDPR, CCPA) and internal audit requirements
- Used Azure DevOps for source control and CI/CD pipelines, automating deployments and streamlining collaboration across teams working on data-related tasks
- Integrated external data sources (such as weather data for risk assessment) using REST APIs, enabling the insurance company to assess external risks more effectively
- Optimized the performance of large datasets by fine-tuning database indexes and partitioning strategies, ensuring fast query performance even with vast amounts of insurance data
- Analyzed business requirements from stakeholders and translated them into actionable technical specifications, ensuring that the data solutions provided critical insights into claims management and customer behavior
- Developed and maintained documentation for all data pipelines, stored procedures, and BI reports, ensuring easy handoff and maintaining project continuity for new team members
- Implemented machine learning models within the Azure ecosystem to predict insurance claim outcomes, reducing fraud and improving claim processing efficiency
- Provided production support, including troubleshooting and optimizing data pipelines, ensuring 99.9% uptime for critical data integration systems that supported business operations
- Built a real-time data streaming pipeline using Azure Stream Analytics to ingest real-time claims data, enabling faster decision-making and more efficient claim handling
- Refined business intelligence solutions by integrating predictive analytics for customer retention and risk assessment, enabling more accurate forecasting and improved business decisions
- Managed large-scale data migration efforts, transitioning from on-premise databases to Azure-based solutions, reducing costs and improving data processing capabilities
- Utilized Power Query and Power Apps to streamline data ingestion and analysis for internal insurance analysts, improving business agility and reporting speed
- Engaged in Agile sprint planning, participating in daily stand-ups, sprint reviews, and retrospectives, ensuring continuous improvement and aligning work with business priorities
- Optimized data storage strategies by incorporating partitioning and indexing techniques, reducing query times and improving overall system efficiency for analytics workloads
- Monitored data pipeline performance, proactively identifying and addressing bottlenecks in data workflows, ensuring high availability and optimal system performance for business users
- Built and maintained data lineage documentation to track the flow of data from source to destination, improving traceability and ensuring data integrity throughout the lifecycle
- Integrated automated alerts into the data pipeline to notify stakeholders of potential data quality issues or performance bottlenecks, enabling rapid response and issue resolution
- Collaborated with data scientists to implement A/B testing on claim processing algorithms, ensuring that new models were thoroughly tested before deployment
- Ensured compliance with industry standards and internal policies on data security, working with security teams to implement encryption and access control policies for sensitive insurance data
- Developed custom API integrations for retrieving external datasets (such as financial market data) that were critical to assessing claim risk and pricing models
- Mentored junior data engineers on best practices for ETL design, SQL optimization, and data modeling, helping to elevate the team's overall technical skill set
- Maintained high standards of data privacy, ensuring that sensitive information (such as customer PII and claim details) was securely handled in compliance with data protection laws
Data research Engineer Analyst
Purdue University
Hammond, United States
02.2019 - 01.2020
- Developed and maintained market research libraries and created summaries and recommendations
- Created mechanisms for tracking effectiveness of pricing initiatives by evaluating performance and attrition of all impacted merchants
- Excellent knowledge of relevant programming languages such as SQL and Python
- Good knowledge of basic statistics, statistical software such as SPSS, and spreadsheet tools such as Excel
- Created Data Visualization, visually compelling charts that will help data readable
- Developed Data Cleaning consists of retrieving data from one source to another, handling missing data and inconsistent data that affect analysis
- Using ETL tools, Collecting and transforming data through data preparation, integration, cloud pipeline design
- Executed Data Analysis and Data Visualization on survey data using Tableau Desktop as well as Compared respondent's demographics data with Univariate Analysis using Python (Pandas, NumPy, Seaborn, Sklearn, and Matplotlib)
- Developed a machine learning model to recommend friends to students based on their similarities
- Used Alteryx for Data Preparation in such way that is it useful for developing reports and visualizations
- Analyzed university research budget with peer universities budgets in collaboration with the research team, and recommended data standardization and usage to ensure data integrity
- Helped with pattern recognition of financial time series data sources and forecasting of returns
Data Engineer Analyst, SQL Developer
AMAZON.COM
Hyderabad, India
05.2016 - 05.2018
- Assist in database designing, writing views, stored procedures and query optimization using SQL
- Predictive Modeling and Data Visualization using Python and Tableau
- Departments to establish KPIs and monitor success of new algorithms and products
- Created dashboards based on in-depth weekly, monthly, quarterly and annual metrics reporting of current online traffic, conversion trends and highlights of ongoing marketing campaigns using Tableau
- Collected, consolidated and analyzed data/metrics from multiple sources such as site analytics and Store analytics
- Performed statistical analysis including univariate, bivariate and multivariate methods, Logistic regression in Python to analyze company's data and E-commerce website to identify business trends and create visual outputs for business to make decisions
- Built various graphs for business decision making using Python matplotlib library
- Worked in development of applications especially in UNIX environment and familiar with all its commands
- Handling the day-to-day issues and fine tuning the applications for enhanced performance
- Assisted the senior team with Business Intelligence tools such as QlikView and Business Objects to develop reports and provide data to stakeholders
SQL Developer
GSS America
, India
05.2014 - 04.2016
- Create and maintain database for Server Inventory, Performance Inventory
- Working with SQL, and T-SQL, VBA
- Involved in creating tables, stored procedures, indexes
- Creating and Maintain users
- Creating / Running jobs with packages
- Design, Develop, Deploy Packages with WMI Queries
- Importing Data from various sources like Excel, SQL Server, Front base
Education
Master of Science - Computer Information Technology
Purdue University
Hammond, IN06.2020
Skills
- ETL development
- Data pipeline design
- Data security
- Data integration
- Machine learning
- Scala programming
Certification
- Microsoft Certified: Azure Fundamentals
- AWS Certified Solutions Architect
- Information Security: Context and Introduction
- NoSQL and DBaaS
- 101 Data Management Professional
Personal Information
Title: Sr. Data Engineer
Timeline
Lead Data Engineer
Bank Of America
03.2024 - Current
Lead Data Engineer
Medline Industries
10.2023 - 02.2024
Sr. Data Engineer
CVS Pharmacy
10.2020 - 10.2023
Data research Engineer Analyst
Purdue University
02.2019 - 01.2020
Data Engineer Analyst, SQL Developer
AMAZON.COM
05.2016 - 05.2018
SQL Developer
GSS America
05.2014 - 04.2016
Master of Science - Computer Information Technology
Purdue University
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