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Sravani Vadlamudi - Data Engineer

[email protected]

Location: Remote, Remote, USA

Relocation: yes

Visa: GC

SRAVANI VADLAMUDI Phone: (504)315-5220 | E-Mail: [email protected] Sr. Data Engineer/Analyst Professional Experience: Over 10+ years of Experienced Data Engineer III with a proven track record of designing and implementing robust and scalable data solutions for reporting and analytics. Specialized in Azure Databricks, SQL, Spark, Python, and Scala, with a strong focus on building complex business logic. Seeking a challenging role to contribute expertise in data engineering, ETL processes, and cloud-based solutions. Senior Data Engineer with 10+ years of experience delivering enterprise data solutions for state and federal healthcare programs. Extensive expertise in supporting large-scale Medicaid and Medicare data ecosystems within highly regulated environments. Proven ability to maintain and modernize legacy SQL Server BI platforms, including SSIS, SSAS, and SSRS. Strong hands-on experience managing high-volume claims, provider, eligibility, and member data. Demonstrated leadership in data warehouse operations and mission-critical reporting systems. Expert in advanced T-SQL development, performance tuning, and query optimization. Hands-on experience designing, troubleshooting, and enhancing complex SSIS ETL pipelines. Skilled in supporting federal and state compliance reporting with zero operational downtime. Deep expertise in Azure cloud data engineering and modernization initiatives. Proficient in building Azure Databricks Lakehouse solutions using PySpark and SQL. Strong implementation experience with Delta Lake, Unity Catalog, and Medallion Architecture (Bronze/Silver/Gold). Advanced knowledge of Azure Synapse Analytics, including Dedicated and Serverless SQL Pools. Experienced in refactoring legacy ETL logic into Azure Data Factory and Databricks pipelines. Proven success migrating source control and CI/CD workflows from TFS to Azure DevOps (Git). Strong background in end-to-end system engineering lifecycle and large-scale project delivery. Hands-on expertise in SQL and Python (PySpark) for scalable data processing. Extensive experience working with Medicaid data models, MMIS systems, and EDI 837/835 transactions. Strong understanding of HIPAA, CMS MARS-E, and NIST security standards. Experienced in implementing Row-Level Security (RLS) and automated data masking for PHI/PII protection. Proven ability to establish data governance, quality, and validation frameworks. Skilled in ensuring data parity between legacy SQL systems and modern cloud platforms. Experience working closely with architects, stakeholders, and cross-functional teams. Demonstrated consulting leadership in highly regulated government environments. Strong documentation, communication, and stakeholder engagement skills. Committed to delivering secure, reliable, and future-ready data platforms for public sector healthcare systems. Technical Skills: SQL Server (SSIS, SSAS, SSRS) Expert T-SQL & performance tuning Team Foundation Server (TFS) Azure Databricks (PySpark, Delta Lake, Unity Catalog) Azure Synapse (Dedicated & Serverless SQL Pools) Azure Data Factory (ADF) Medallion Architecture (Bronze/Silver/Gold) Azure DevOps (CI/CD, Git) Medicaid / Medicare (MMIS, Claims, Eligibility, Providers) EDI 837 / 835 HIPAA, CMS MARS-E, NIST Row-Level Security (RLS), Data Masking Microsoft Purview (Data Catalog) SQL, Python (PySpark), Scala Professional Experience: Client: AbbVie, Remote Sep 2023 to till now. Role: Senior Data Engineer / Analyst Roles & Responsibilities: Lead enterprise-scale data modernization initiatives supporting regulated healthcare analytics platforms. Maintain, enhance, and optimize SQL Server BI assets (SSIS, SSAS, SSRS) supporting mission-critical reporting. Support high-volume healthcare datasets with strict uptime, accuracy, and audit requirements. Design, troubleshoot, and enhance complex SSIS ETL pipelines for operational and analytical workloads. Perform advanced T-SQL development and performance tuning for large relational datasets. Refactor legacy on-prem ETL logic into Azure Databricks notebooks using PySpark and SQL. Implement Medallion Architecture (Bronze/Silver/Gold) within Azure Databricks Lakehouse. Develop scalable pipelines using Azure Data Factory (ADF) for batch and incremental processing. Implement Delta Lake for ACID-compliant storage and reliable data versioning. Configure Unity Catalog for centralized metadata management and access control. Build and optimize data models to support analytics, reporting, and downstream integrations. Develop CI/CD pipelines in Azure DevOps, migrating workflows from legacy version control systems. Automate build, release, and deployment processes across DEV, QA, and PROD environments. Integrate Azure Key Vault for secrets management and secure credential handling. Enforce PHI/PII protection through Row-Level Security (RLS) and automated data masking. Support healthcare compliance requirements aligned with HIPAA and federal regulations. Validate data accuracy and consistency between legacy SQL platforms and cloud-based systems. Implement data quality checks and reconciliation frameworks for regulated reporting. Collaborate closely with architects, analysts, and business stakeholders on solution design. Provide technical leadership and mentorship to junior data engineers. Participate in design reviews, audits, and governance discussions. Produce detailed technical documentation for pipelines, models, and operational processes. Support production issue resolution and root cause analysis for data-related incidents. Ensure secure, reliable, and scalable data delivery for enterprise healthcare analytics. Environment: SQL Server (SSIS, SSAS, SSRS), T-SQL, Azure Databricks, PySpark, Delta Lake, Unity Catalog, Azure Synapse, Azure Data Factory, Azure DevOps, Git, Azure Key Vault, HIPAA, CI/CD, Healthcare Data Systems Client: Maximus, McLean, VA Oct 2021 to Aug 2023 Role: Senior Data Engineer Roles & Responsibilities: Served as Senior Data Engineer supporting state and federal Medicaid data platforms. Worked on large-scale Medicaid claims, eligibility, provider, and enrollment datasets. Maintained and enhanced complex SQL Server SSIS ETL pipelines processing high-volume healthcare data. Supported SSRS reports and SSAS tabular/multidimensional models used for CMS and state reporting. Ensured uninterrupted delivery of mission-critical Medicaid reports for regulatory compliance. Performed advanced T-SQL development and performance tuning across SQL Server environments. Diagnosed and resolved production issues in ETL, reporting, and data warehouse layers. Led modernization efforts migrating on-prem SQL Server workloads to Azure cloud platforms. Designed and developed scalable data pipelines using Azure Databricks (PySpark, SQL). Implemented Azure Synapse Analytics to support advanced analytics and reporting workloads. Applied Medallion Architecture (Bronze/Silver/Gold) for structured data processing. Refactored legacy SSIS logic into Azure Data Factory (ADF) pipelines and Databricks notebooks. Managed source control using Team Foundation Server (TFS) for legacy environments. Led migration from TFS to Azure DevOps (Git), enabling modern CI/CD practices. Built and automated CI/CD pipelines for SQL Server, SSIS, and Azure components. Implemented deployment strategies across DEV, QA, and PROD environments. Developed data reconciliation and validation frameworks to ensure legacy-to-cloud parity. Ensured data accuracy, completeness, and consistency across Medicaid reporting systems. Applied HIPAA-compliant data governance and security controls for PHI/PII. Supported CMS reporting standards and audit requirements. Collaborated closely with state agencies, business analysts, and compliance teams. Participated in federal and state audits, reviews, and compliance assessments. Documented ETL processes, data models, and system workflows. Provided technical leadership and mentoring to junior engineers. Ensured scalable, secure, and compliant delivery of Medicaid analytics solutions. Environment: SQL Server (SSIS, SSAS, SSRS), T-SQL, Azure Databricks, PySpark, Azure Synapse Analytics, Azure Data Factory, Team Foundation Server (TFS), Azure DevOps (Git), CI/CD, HIPAA, CMS Reporting, Medicaid MMIS Data Client: CITI Bank,NY. July 2020 to Sep 2021 Role: Data Engineer Roles & Responsibilities: Used ETL DataStage Director for scheduling, running jobs, testing, debugging, and monitoring performance statistics. Installed Hadoop, MapReduce, HDFS, AWS, and developed MapReduce jobs in PIG and Hive for data cleaning and pre-processing. Worked on batch processing of data sources using Apache Spark and Elastic Search. Migrated PIG scripts and MapReduce programs to Spark DataFrames API and Spark SQL for improved performance. Developed Big Data solutions focusing on pattern matching and predictive modeling. Architected, designed, and developed business applications and Data marts for reporting. Involved in different phases of the development life cycle, including analysis, design, coding, unit testing, integration testing, review, and release. Implemented Spark GraphX application for analyzing guest behavior in data science segments. Participated in a highly immersive Data Science program involving data manipulation, visualization, web scraping, machine learning, Python programming, SQL, GIT, Unix Commands, NoSQL, MongoDB, and Hadoop. Performed scoring and financial forecasting for collection priorities using Python and SAS. Developed predictive causal models using annual failure rate and standard cost basis. Managed existing team members and led the recruitment and onboarding of a larger Data Science team. Collaborated with ETL Developers, business, and IT management in designing and planning ETL requirements for reporting. Collaborated with a cross-functional team to frame and answer important data questions. Designed, developed, and deployed Python modeling APIs for customer analytics, integrating multiple machine learning techniques. Designed, developed, and maintained data integration programs in Hadoop and RDBMS environments. Participated in normalization/de-normalization, normal form, and database design methodology. Used data modeling tools like MS Visio and Erwin Tool for logical and physical design of databases. Worked on Data modeling, Advanced SQL with Columnar Databases using AWS. Created Hive queries and tables to identify trends by applying strategies on historical data. Extensively used Apache Sqoop for transferring bulk data between Apache Hadoop and relational databases (Oracle). Designed and implemented end-to-end systems for Data Analytics and Automation, integrating custom visualization tools using R, Tableau, and Power BI. Environment: IBM DataStage, Python, Spark framework, AWS, Redshift, MS Excel, NoSQL, Tableau, T-SQL, ETL, RNN, LSTM MS Access, XML, MS office 2007, Outlook, MS SQL Server. Client Name: State of NE. Feb 2018 to June 2020 Role: Data Engineer Roles & Responsibilities: Utilize Scala, Python, and SQL to design, develop, and optimize Extract, Transform, Load (ETL) processes for large-scale data sets. Implement efficient data integration workflows using Stream sets and Apache Kafka. Leverage Apache Spark on Azure Databricks to process and analyze massive datasets, ensuring optimal performance and scalability. Design and implement data processing solutions to support real-time and batch analytics. Architect and maintain end-to-end data pipelines, ensuring the seamless flow of data from various sources to the destination systems. Collaborate with cross-functional teams to design scalable and maintainable data architectures. Conduct performance tuning of Spark jobs and ETL processes to enhance overall system efficiency and reduce processing times. Optimize SQL queries for improved database performance. Implement data quality checks and validation processes to ensure the accuracy, completeness, and reliability of the data. Develop and maintain monitoring solutions to proactively identify and address data quality issues. Design & implement real-time data streaming solutions using Apache Kafka for capturing and processing streaming data. Ensure the reliability and fault-tolerance of real-time data pipelines. Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver effective solutions. Create and maintain comprehensive documentation for data engineering processes, workflows, and data models. Implement and enforce data security measures, ensuring the confidentiality and integrity of sensitive data. Ensure compliance with relevant data privacy regulations and industry standards. Conduct root cause analysis for data-related issues and implement corrective actions to prevent reoccurrence. Troubleshoot and debug complex data engineering problems across the entire data stack. Stay abreast of the latest advancements in data engineering, big data, and analytics technologies. Evaluate and recommend new tools and frameworks that can enhance the efficiency and capabilities of the data engineering ecosystem. Integrate Qlik into the data engineering ecosystem to facilitate business intelligence and reporting requirements. Collaborate with business intelligence teams to ensure seamless integration and optimal performance. Manage and optimize data storage on Microsoft Azure, specifically Azure Cloud and Azure Data Lake Storage Gen2. Implement efficient storage solutions using Azure SQL Data Warehouse, Azure Data Lake, and Azure Cosmos DB. Utilize GIT for source code control to manage and track changes in data engineering processes and code. Collaborate with development teams to ensure version control best practices are followed. Apply expertise in data warehousing transformation processes and architecture to design and maintain robust data warehousing solutions. Optimize data warehousing processes for performance and scalability. Assess the benefits and risks associated with data assets, providing insights to stakeholders. Collaborate closely with security teams to implement and enforce data security measures, ensuring compliance with industry standards. Lead the definition and design of logical data models to represent business entities and relationships. Develop and maintain data models from conceptual through physical, ensuring alignment with business requirements. Design and implement canonical data views to establish a common data representation across the organization. Ensure consistency and reusability of data models for various applications and systems. Establish and enforce data governance practices to ensure data quality, integrity, and compliance. Define and implement metadata management processes to track and catalog data assets. Evaluate emerging technologies and recommend their adoption based on their potential impact on data engineering and analytics capabilities. Environment: Java, Scala, Python, SQL, Big Data and Analytics, Azure Databricks, Apache Spark 3, Data Integration and ETL Tools, Apache Kafka Client Name: Wells Fargo,TX Mar 2014 to Jan 2017 Role: Big Data Developer Roles & Responsibilities: Worked on Agile, testing methodologies, resource management and scheduling of tasks. Worked on all phases of data warehouse development lifecycle, from gathering requirements to testing, implementation, and support. Worked with Amazon Web Services (AWS) for improved efficiency of storage and fast access. Worked on Data migration project from Teradata to Snowflake. Developed data pipeline using Sqoop to ingest cargo data and customer histories into HDFS for analysis. Worked on AWS Redshift and RDS for implementing models and data on RDS and Redshift. Implemented Data pipelines for big data processing using Spark transformations and Python API and clusters in AWS. Specified nodes and performed the data analysis queries on Amazon redshift clusters on AWS. Involved in developing ETL pipelines in and out of data warehouse using combination of Python and Snowflakes SnowSQL. Developed MapReduce programs to parse the raw data, populate staging tables and store the refined data in partitioned tables in the EDW. Added support for Amazon AWS S3 to host static/media files and the database into Amazon Cloud. Used Oozie to automate data loading into the HDFS and PIG to pre-process the data. Called Data Rules and Transform Rules functions using Informatica Stored Procedure Transformation. Implemented data models, database designs, data access, table maintenance and code changes together with team. Joined various tables in Cassandra using spark and Scala and ran analytics on top of them. Analyze data from multiple data sources and develop a process to integrate the data into a single but consistent view. Performed Data Analytics on Data Lake using Pyspark on Databricks platform. Added support for Amazon AWS S3 and RDS to host static/media files and the database into Amazon Cloud. Day to-day responsibility includes developing ETL Pipelines in and out of data warehouse, develop reports using advanced SQL queries in snowflake. Environment: Agile, Snowflake, AWS, Spark, HDFS, Sqoop, Oozie, Scala, Cassandra, PIG, HBase, Tableau, Excel, Pyspark, Databricks, Informatica. Keywords: continuous integration continuous deployment quality analyst business intelligence sthree database rlang information technology microsoft mississippi Delaware Nebraska New York Texas Virginia