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Navya Sri - Azure data engineer

[email protected]

Location: Aurora, Illinois, USA

Relocation: yes

Visa: H1B

Name: Navya Sri. B Ph no: +1 331-256-7273 | Gmail: [email protected] LinkedIn: https://www.linkedin.com/in/navya-sri-brahmandaberi-5ab59726b/ SENIOR AZURE DATA ENGINEER PROFESSIONAL SUMMARY Over 8+ years of experience in Data Engineering and BI Engineering, building ETL/ELT pipelines, data conversion, data migration, and operational analytics across Microsoft Azure, Google Cloud Platform (GCP), and AWS for Healthcare (Payer/Provider), Banking, Financial Services, Insurance, and Retail clients. Hands-on experience designing, developing, and maintaining ETL/ELT pipelines using Azure Data Factory (ADF), Azure Databricks (PySpark), SSIS, and Informatica, with Azure Data Lake Storage Gen2 (ADLS), Azure SQL Database, Azure Synapse Analytics, Snowflake, and lakehouse architecture. Strong experience with SQL Server and advanced SQL: developing and optimizing queries, views, and stored procedures that support reporting and analytics, with performance tuning of high-volume workloads. Skilled in building Power BI dashboards with data models, DAX calculations, and publishing, and in developing SSRS parameterized, drill-down, and operational reports, along with Tableau and Looker for leadership dashboards. Experienced in data ingestion from multiple sources and file formats (CSV, JSON, Excel, Parquet, REST APIs), with data validation, reconciliation, data quality checks, and root-cause analysis of pipeline failures. Hands-on experience with the Salesforce Data Model, Schema, Objects (Account, Contact, Opportunity, Case, Lead), Lookup and Master-Detail Relationships, and Salesforce-to-partner data flows using REST APIs, SOQL, and ADF connectors. Experience in AI/ML data preparation and analytics use cases, including feature datasets, MLflow tracking, and model monitoring for payer and clinical analytics. Strong in data security and compliance (HIPAA, PHI, PCI-DSS, SOX, GDPR); work in Agile/Scrum teams following DevOps and DataOps best practices with Product Owners, Architects, Salesforce Administrators, IS teams, and business stakeholders. TECHNICAL SKILLS Big Data Technologies Hadoop, MapReduce, Spark, HDFS, Sqoop, YARN, Oozie, Hive, Impala, Zookeeper, Apache Flume, Apache Airflow, Cloudera, HBase, Rally, Jira Programming Languages Python, PySpark, SQL, T-SQL, PL/SQL, SOQL, Scala, C, C#, C++, PowerShell Scripting, JavaScript, Perl Script, DAX Cloud Technologies Microsoft Azure, AWS, GCP, Databricks, Snowflake, Lakehouse Architecture Cloud Services Azure Data Factory (ADF), Azure Data Lake Storage Gen2 (ADLS), Azure SQL Database, Azure Synapse Analytics, Blob Storage, Azure Event Hubs, Delta Tables, AWS RDS, Amazon SQS, Amazon S3, AWS EMR, Lambda, AWS SNS, Dataflow, BigQuery, Cloud Functions ETL Tools SSIS, Informatica, Azure Data Factory, Databricks (PySpark), Data Stage, Talend Reporting & BI Power BI (Data Modeling, DAX, Publishing), SSRS (Parameterized, Drill-Down, Operational Reports), Tableau, Looker, QuickSight Databases SQL Server, Azure SQL, Oracle, MySQL, IBM DB2, Postgres, Teradata, Snowflake, MS Access; Stored Procedures, Views, Query Optimization NoSQL Databases MongoDB, Cassandra DB, HBase File Formats & Ingestion CSV, JSON, Excel, Parquet, Delta, REST API Ingestion, Batch and Streaming Ingestion Salesforce Salesforce Data Model, Salesforce Schema, Standard & Custom Objects, Lookup and Master-Detail Relationships, Junction Objects, SOQL, Salesforce Reporting, Salesforce-to-Partner System Data Flows Development Strategies Agile, Scrum, Lean Agile, DevOps, DataOps, Pair Programming, Waterfall, Test-Driven Development Version Control & Containerization Azure DevOps, Jenkins, Git, CircleCI, SVN, Docker, Kubernetes Monitoring Tools Apache Airflow, Control-M, CloudWatch, Azure Monitor Tools PyCharm, Eclipse, Visual Studio, SQL Server Management Studio (SSMS), SQL Developer, TOAD, SQL Navigator, Query Analyzer, Postman WORK EXPERIENCE IBA (Ion Beam Application) Jul 2024 - Present Senior Data Engineer Contributed throughout the complete SDLC including requirement gathering, solution design, data modeling, ETL/ELT pipeline development, data conversion, data migration, testing, deployment, and production support for cloud-native healthcare AI and data engineering initiatives in Agile teams using DevOps and DataOps practices. Designed and implemented data conversion and migration pipelines on GCP using Dataflow, BigQuery, and Cloud Storage, processing 12TB+ of medical imaging, treatment planning, device telemetry, and clinical data, including Salesforce-to-partner clinical system data flows with Salesforce Data Model alignment, schema mapping, and object relationship validation. Developed ETL/ELT pipelines using SSIS packages, Informatica, and Azure Data Factory (ADF) to ingest CSV, JSON, and Excel files from clinical, device, and partner sources into SQL Server and Azure SQL Database, with incremental loads, error handling, and logging. Wrote and optimized advanced SQL, T-SQL stored procedures, and views in SQL Server and Azure SQL Database to support reporting and analytics, reducing report query runtimes through indexing, execution-plan analysis, and performance tuning. Built Power BI dashboards with star-schema data models, DAX measures, row-level security, and workspace publishing, plus Tableau dashboards, giving Product Owners, Architects, and leadership visibility into pipeline health, data quality metrics, and clinical data trends. Developed SSRS parameterized, drill-down, and operational reports on SQL Server data, with scheduled subscriptions, for Back Office and Middle Office stakeholders. Built reusable ETL utilities and pipeline frameworks using Vertex AI Pipelines, Kubeflow, and Cloud Composer, reducing pipeline development and deployment time by 60% while enforcing reproducibility and governance. Built real-time pipelines using Pub/Sub, Dataflow, Cloud Run, and Vertex AI Endpoints for medical device telemetry analysis, predictive maintenance, and clinical alerts with 99.9% platform availability; prepared AI/ML datasets for model training and analytics. Implemented data quality frameworks covering data validation, reconciliation checks, and automated root-cause analysis on structured and unstructured healthcare datasets, improving data accuracy by 35%; troubleshot Salesforce-integrated pipelines for schema mismatches, object relationship errors, and ETL failures. Developed Salesforce (Account, Contact, Case, custom objects) integrations with Back Office and Middle Office healthcare systems using REST APIs, SOQL, and ADF connectors. Implemented security using IAM, Cloud KMS, Secret Manager, VPC Service Controls, and audit logging for PHI protection and compliance with HIPAA, GDPR, and medical device security requirements. Environment: GCP, Vertex AI, BigQuery, Dataflow, Pub/Sub, Cloud Composer, Cloud Run, Kubeflow, MLflow, Docker, GKE, Azure Data Factory, SSIS, Informatica, SQL Server, Azure SQL Database, T-SQL, Stored Procedures, SSRS, Power BI, DAX, Tableau, Salesforce, SOQL, REST APIs, Python, CSV, JSON, Excel OPTUM, IL Apr 2023 - June 2024 Sr. Data Engineer / ML Engineer Designed and deployed ETL/ELT pipelines using Azure Data Factory (ADF), Azure Databricks (PySpark), SSIS, Informatica, and ADLS Gen2 to process 10TB+ of payer claims, member, provider, pharmacy, and care management data, integrating Salesforce data flows with Back Office and Middle Office healthcare systems. Built SSIS packages with control flow, data flow, lookup, and conditional split transformations, plus Informatica mappings, to migrate legacy SQL Server and Oracle data into Azure SQL Database and Azure Synapse Analytics, with restartable loads and audit logging. Ingested claims, eligibility, and provider data from multiple sources and file formats (CSV, JSON, Excel, flat files, REST APIs) into ADLS Gen2 and Snowflake, applying schema validation, data reconciliation, and PHI masking. Developed and tuned advanced SQL, T-SQL stored procedures, views, and indexes in SQL Server and Azure SQL Database for claims analytics, cutting reporting query execution time through execution-plan analysis and partitioning. Created Power BI dashboards with data models, relationships, DAX calculations (time intelligence, KPIs), row-level security, and workspace publishing, surfacing claims analytics, provider performance, and care management KPIs for Product Owners and Business Stakeholders. Developed SSRS parameterized, drill-down, and operational reports (claims status, provider performance, SLA reports) with scheduled subscriptions, built on SQL Server stored procedures, and Tableau dashboards for leadership reporting. Built reusable ETL utilities using Azure Databricks (PySpark), Delta Lake, and Azure Synapse Analytics in a lakehouse architecture, improving data transformation performance by 55%; prepared AI/ML feature datasets for payer analytics models. Built operational analytics and production monitoring using Azure Monitor, MLflow, and Azure Machine Learning to track pipeline health, model performance, data drift, and SLA adherence; applied Azure DevOps CI/CD and DataOps practices for release automation. Built and maintained Salesforce (Account, Contact, Case, Opportunity, custom healthcare objects) integrations using ADF Salesforce connectors, SOQL, and REST APIs, mapping schema, validating Lookup and Master-Detail relationships, and performing root-cause analysis on integration failures. Implemented Responsible AI and governance practices (SHAP, Azure Responsible AI Dashboard, bias detection), and secured environments with Azure Key Vault, Microsoft Entra ID, RBAC, private endpoints, encryption, and data masking for HIPAA compliance and PHI protection, working in Agile teams. Environment: Azure Data Factory (ADF), Azure Databricks, PySpark, SSIS, Informatica, SQL Server, Azure SQL Database, T-SQL, Stored Procedures, Azure Synapse Analytics, ADLS Gen2, Snowflake, Delta Lake, Power BI, DAX, SSRS, Tableau, Azure Machine Learning, MLflow, Azure DevOps, AKS, Azure Monitor, Azure Key Vault, Microsoft Entra ID, Terraform, Git, Docker, Python, Scikit-learn, XGBoost, LightGBM, Salesforce, SOQL, REST APIs Hasbro, IL May 2022 - March 2023 Data Engineer Designed and implemented scalable data ingestion and conversion pipelines using Azure Data Factory (ADF), Azure Databricks, and Azure Event Hubs to process 10TB+ of daily retail, e-commerce, inventory, and point-of-sale (POS) data, reducing data latency by 45%. Developed and maintained data integrations between Salesforce (Account, Contact, Opportunity, Lead, and custom retail objects) and Back Office/Middle Office enterprise systems (SAP, Oracle, SQL Server) using ADF Salesforce connectors and SOQL, mapping Salesforce Objects, Relationships, and Schema to downstream data models. Built data flows between Salesforce and partner systems including e-commerce platforms, third-party logistics, and ERP applications, performing data validation, quality checks, and root-cause analysis on schema mismatches, object relationship errors, and integration failures. Developed Tableau dashboards and leadership reporting solutions for sales reporting, demand forecasting, and executive data insights, supporting Product Owners, business stakeholders, and senior leadership. Developed and optimized ETL/ELT workflows using Azure Databricks (PySpark) and Azure Synapse Analytics, improving data transformation performance by 55% while supporting enterprise-wide sales, merchandising, and supply chain analytics. Designed dimensional data models and optimized warehouse performance in Azure Synapse Analytics, reducing complex analytical query execution time by 60% for sales reporting, demand forecasting, and executive dashboards. Automated orchestration, scheduling, and production monitoring of 700+ daily data pipelines using Azure Data Factory with 99.8% pipeline reliability, automated error handling, and alerting. Implemented enterprise data quality frameworks using Great Expectations, PySpark, and custom validation rules, improving data accuracy by 35% across product, customer, inventory, and order management datasets. Enforced enterprise security, governance, and compliance using Azure Key Vault, Microsoft Entra ID (Azure AD), RBAC, private networking, encryption at rest/in transit, and data masking. Collaborated with Product Owners, Architects, Salesforce Administrators, IS teams, merchandising teams, and business stakeholders in Agile environments to design and deliver production-ready data platforms. Environment: Azure Data Factory (ADF), Azure Databricks, PySpark, Azure Synapse Analytics, ADLS Gen2, Azure Event Hubs, Azure Stream Analytics, Delta Lake, Azure DevOps, CI/CD, AKS, Azure Key Vault, Azure Monitor, SQL Server, Oracle, SAP, Salesforce, SOQL, Tableau, Python, Scala, Great Expectations, REST APIs, Git, RBAC, PCI-DSS, SOX, ETL, ELT Cognizant, Hyderabad, India July 2017 - Dec 2021 Data Engineer Built end-to-end data conversion and migration pipelines using AWS Glue, streamlining ETL/ELT processes for multi-terabyte datasets and supporting enterprise data migrations from legacy systems to AWS. Designed and maintained data integrations between Salesforce and enterprise partner systems using REST APIs and SOQL, mapping Salesforce Objects, Relationships, and Schema to downstream SQL-based data warehouses (Redshift, Aurora) for operational analytics and reporting. Developed operational analytics and production monitoring solutions using CloudWatch, SNS, and AWS QuickSight, building dashboards and leadership reports to track pipeline health, SLA adherence, and business KPIs. Built Tableau dashboards and reporting solutions integrated with AWS Redshift and S3, enabling real-time executive dashboards and operational data insights for business stakeholders. Optimized AWS Redshift data warehouses, improving query performance by 45% through distribution and sort key strategies; implemented Slowly Changing Dimensions (SCD) for accurate historical data tracking. Performed data validation, quality checks, troubleshooting, and root-cause analysis on pipeline failures, schema mismatches, and data integrity issues across SQL, NoSQL, and Salesforce-integrated datasets. Leveraged AWS EMR with Apache Spark and PySpark to process large-scale datasets, reducing processing time by 35%; designed data Lakehouse architecture using AWS S3 and Athena for advanced analytics. Implemented real-time data streaming using AWS Kinesis and Firehose; orchestrated complex ETL pipelines with AWS Glue and Step Functions integrating diverse data sources (SQL, NoSQL, Salesforce, APIs). Established data governance frameworks using AWS Lake Formation for metadata management and compliance; configured AWS IAM and KMS for secure data access and encryption across pipelines. Collaborated with cross-functional teams including Product Owners, Architects, IS teams, and Business Stakeholders to align data engineering solutions with business objectives in Agile environments. Environment: Python, Spark, AWS EC2, S3, EMR, Redshift, Glue, RDS, Kinesis Firehose, Kinesis Data Streams, SNS, SQS, Athena, Snowflake, SQL, SOQL, Tableau, Salesforce, REST APIs, Git, Jira, CloudWatch EDUCATION Bachelor's in Computer Science and Engineering - JNTUH, 2017 Keywords: cprogramm cplusplus csharp continuous integration continuous deployment artificial intelligence machine learning business intelligence sthree database active directory microsoft mississippi procedural language Idaho Illinois