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Navya sree - SR. DATA ENGINEER

[email protected]

Location: Memphis, Tennessee, USA

Relocation:

Visa: GC

Resume file: NAVYA SREERAMA _ DATA ENG_1749651102191.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

SR. DATA ENGINEER NAVYA SREERAMA +1 712-724-8014 [email protected] Career Objective: Results-driven and technically skilled Data Engineer with over 10 years of experience in designing, developing, and managing scalable, high-performance data infrastructures across healthcare, banking, and financial services domains. Committed to leveraging cloud-native platforms, distributed data systems, and modern data engineering practices to deliver reliable, secure, and real-time data solutions that empower business intelligence and advanced analytics. Eager to contribute in a collaborative environment where innovation, data accuracy, and performance matter. Professional Summary: o Accomplished Data Engineering professional with over 10 years of hands-on experience delivering scalable and automated data solutions in enterprise-grade environments. o Proven expertise in building complex ETL and ELT pipelines using tools like Apache Spark, AWS Glue, and Talend, handling multi-terabyte datasets across structured and semi-structured formats. o Highly skilled in designing cloud-native data lakes and warehouses using AWS (S3, Redshift), GCP (BigQuery), and Azure Data Lake for optimized analytics and cost efficiency. o Deep understanding of data architecture, dimensional modeling, and schema design (star, snowflake, normalized/denormalized) for building robust data marts. o Expertise in batch and streaming data processing using Kafka, Kinesis, and Spark Streaming for real-time data workflows and business alerts. o Developed and managed enterprise-scale data pipelines that processed millions of daily transactions in industries such as insurance, banking, and asset management. o Hands-on with data governance, encryption, masking, and PII protection, ensuring regulatory compliance (HIPAA, GDPR, SOX) and end-to-end data lineage. o Worked closely with DevOps and SRE teams to integrate CI/CD and containerized deployment workflows using Docker, Jenkins, and Kubernetes. o Adept at optimizing SQL queries, data warehouse tuning, and query performance troubleshooting in Redshift, Snowflake, and BigQuery. o Delivered robust metadata-driven pipelines, integrating logging, monitoring, alerting, and auto-healing components using tools like Airflow, CloudWatch, and ELK stack. o Extensive experience building self-service reporting systems and collaborating with BI teams using Tableau, Power BI, and Looker. o Strong programming background in Python, Scala, and SQL, applying advanced scripting to enable data transformations, automation, and data quality checks. o Led migration of legacy ETL and warehouse systems to cloud platforms, reducing operational costs and increasing system reliability. o Designed multi-source data integrations, ingesting and transforming data from REST APIs, SFTP, RDBMS, and SaaS applications like Salesforce and Workday. o Instrumental in defining data security models, establishing access control policies via IAM, role-based permissions, and encryption at rest/in transit. o Created and maintained technical documentation, data dictionaries, and lineage maps, facilitating team onboarding and regulatory audit trails. o Committed to continuous learning, actively exploring evolving data tools (Delta Lake, dbt, Iceberg) and industry best practices for high-impact delivery. Technical Skills: Category Tools Languages: Python, Java, Scala, SQL, Shell Big Data: Hadoop, Spark, Hive, Flink, Kafka, HBase Cloud Platforms: AWS (S3, Glue, Redshift, Lambda, EMR), GCP (BigQuery, GCS), Azure (Data Lake, Synapse, Blob Storage) ETL Tools: Talend, AWS Glue, SSIS, Apache NiFi, Informatica Data Warehousing: Snowflake, Redshift, BigQuery, SQL Server, Teradata Data Orchestration: Apache Airflow, Oozie, Luigi Streaming: Kafka, AWS Kinesis, Spark Streaming APIs & Integration: REST, JSON, XML, Postman, Python Requests BI & Visualization: Tableau, Power BI, Looker Version Control & CI/CD: Git, Jenkins, GitLab CI/CD, Docker, Kubernetes Monitoring & Logging: CloudWatch, ELK Stack, Prometheus, Grafana Security: IAM, KMS, Data Encryption, RBAC, HIPAA, GDPR compliance Languages: Python, Java, Scala, SQL, Shell Big Data: Hadoop, Spark, Hive, Flink, Kafka, HBase Cloud Platforms: AWS (S3, Glue, Redshift, Lambda, EMR), GCP (BigQuery, GCS), Azure (Data Lake, Synapse, Blob Storage) ETL Tools: Talend, AWS Glue, SSIS, Apache NiFi, Informatica Data Warehousing: Snowflake, Redshift, BigQuery, SQL Server, Teradata Data Orchestration: Apache Airflow, Oozie, Luigi Streaming: Kafka, AWS Kinesis, Spark Streaming APIs & Integration: REST, JSON, XML, Postman, Python Requests BI & Visualization: Tableau, Power BI, Looker Version Control & CI/CD: Git, Jenkins, GitLab CI/CD, Docker, Kubernetes Monitoring & Logging: CloudWatch, ELK Stack, Prometheus, Grafana Security: IAM, KMS, Data Encryption, RBAC, HIPAA, GDPR compliance Professional Experience: Sr. Data Engineer | BCBS | VA May 2023 Till Date Responsibilities: o Designed and implemented cloud-native, event-driven data pipelines in AWS Glue and Spark, processing healthcare claims and eligibility data at scale. o Integrated multiple data feeds (HL7, EDI 837, X12) into a centralized data lake on AWS S3, enabling downstream BI and actuarial analysis. o Implemented data encryption, access policies (IAM), and row-level security to safeguard PHI and ensure HIPAA compliance. o Developed CDC-based ETL workflows using AWS DMS and Lambda, maintaining data freshness and low-latency ingestion. o Worked with Snowflake to design data marts and optimized views for care management and utilization review teams. o Created metadata-driven orchestration in Apache Airflow, allowing reusable DAGs and dynamic task branching. o Enabled real-time claims adjudication alerts via Kafka streams integrated with Spark Structured Streaming. o Built data profiling and quality frameworks using Deequ and PyDeequ, ensuring conformance to validation rules. o Tuned SQL queries in Snowflake, reducing report generation time and eliminating performance bottlenecks. o Automated data reconciliations between source (Oracle) and target (S3/Redshift) using custom Python scripts. o Configured S3 lifecycle policies to archive processed data, optimizing storage cost while maintaining compliance. o Led weekly stakeholder syncs with the Data Governance and Security teams to review lineage and audit controls. o Conducted DR drills and backup validation using AWS Backup and CloudFormation scripts. o Delivered Tableau dashboards using clean data sets for executive-level healthcare utilization insights. o Integrated FHIR APIs and internal clinical systems into the data lake for population health analytics. o Created Lambda-based alerting system for pipeline failures and anomalies with push notifications. o Used AWS Glue Catalog and Lake Formation for centralized schema and permission management. o Managed data onboarding for new providers with schema evolution support and automated validations. o Created detailed SOPs, pipeline diagrams, and lineage maps for knowledge transfer and compliance audits. Environment: AWS Glue, Spark, Redshift, Airflow, Snowflake, Kafka, Python, S3, Tableau, Lambda, Deequ, HL7, EDI, Jira, Git. Sr. Data Engineer |BMO | NJ March 20 April 22 Responsibilities: o Developed enterprise-grade data pipelines in Apache Spark and Scala, processing credit, deposits, and risk data from legacy mainframe and modern banking platforms. o Led data warehouse modernization by migrating batch ETL workflows from SSIS to Google Cloud BigQuery with dbt for modeling. o Built real-time fraud detection streams using Kafka, enabling downstream applications to monitor suspicious activity instantly. o Ingested data via REST and SOAP APIs from credit scoring systems (FICO, Experian) into the central data lake. o Built robust reconciliation logic in Python to match GL data against operational systems and flag discrepancies. o Implemented multi-layered security using VPC SC, encryption with KMS, and access control with service accounts and IAM roles. o Reduced daily ETL execution time by refactoring Spark jobs with broadcast joins and partition pruning. o Created modular and reusable DAGs in Airflow, reducing ETL development cycles and increasing job transparency. o Configured BigQuery partitioning and clustering, optimizing query performance and cost for regulatory reports. o Worked with Tableau and Looker to create interactive dashboards for finance and operational risk stakeholders. o Established a fully automated testing framework with Pytest for validating transformation logic in pre-production. o Migrated 30+ workflows to CI/CD pipelines using GitLab and Terraform, enabling infrastructure as code (IaC). o Designed archival strategies for long-term data retention using GCS and automated archival routines. o Developed dbt models with snapshots and incremental strategies, improving auditability and traceability. o Enabled lineage tracking via integration with DataHub and Airflow metadata APIs. o Integrated metrics logging into Prometheus and Grafana for performance monitoring and pipeline visibility. o Orchestrated source-to-target mappings and created high-level designs (HLDs) for internal architecture reviews. o Led data engineering workshops for upskilling business teams on cloud tools and analytics readiness. o Contributed to regulatory reporting projects such as Basel III and CCAR using cleansed, governed datasets. Environment: Apache Spark, Scala, BigQuery, dbt, Kafka, GCS, Airflow, Python, GitLab, Terraform, Tableau, Prometheus, Looker, REST APIs. Data Engineer | Aflac Asset Management | NY Jan 2018 Feb 2020 Responsibilities: o Developed robust ETL frameworks in Python and Spark to process daily positions, trades, and portfolio data from Bloomberg, FactSet, and internal trading systems. o Designed and maintained a secure AWS S3-based data lake to store both structured and unstructured asset management data, enabling scalable analytics for investment research. o Created custom ingestion modules for pulling market data and pricing feeds via REST APIs and FTP from third-party vendors like ICE, Morningstar, and Reuters. o Built reusable transformation pipelines in AWS Glue, standardizing asset classification, performance metrics, and risk attribution data across portfolios. o Automated reconciliation between trade blotter systems and custodial data using SQL Server and Python scripts, reducing errors in reporting and audit delays. o Worked on ingesting fixed-income instrument data from Bloomberg and mapping it to internal classifications using fuzzy logic and rule-based transformations. o Built dimension and fact tables in Redshift, supporting OLAP-style querying for asset allocation and portfolio exposure analysis. o Tuned long-running queries and optimized joins, partitioning, and vacuuming strategies in Redshift to enhance reporting speed and reduce storage cost. o Led the data lineage and traceability effort, building audit trails from data sources to BI dashboards for regulatory and internal compliance. o Delivered Power BI dashboards for front-office portfolio managers, showing real-time views of NAV trends, cash flow projections, and risk exposure. o Designed and implemented streaming pipelines using AWS Kinesis for market ticker data, delivering sub-second refresh times on dashboards. o Integrated data pipelines with enterprise data catalog tools to ensure consistent metadata tagging and schema registration. o Applied role-based access control (RBAC) and KMS encryption to control access to sensitive investment and client data across cloud services. o Established CI/CD pipelines via Jenkins and Git, with unit tests for transformation logic and alerting on pipeline failures. o Conducted quarterly data quality reviews with asset managers, addressing anomalies using automated profiling tools and rule-based alerts. o Wrote SQL-based macros to calculate asset performance, net flows, and benchmark comparisons in Redshift and Python notebooks. o Authored documentation for technical workflows and business data mappings, supporting onboarding and handovers to other teams. Environment: AWS Glue, Spark, Redshift, Python, SQL Server, Power BI, Jenkins, Git, Kinesis, Docker, S3, Bloomberg APIs, REST APIs, Agile. Jr. Data Engineer |Micro land | India May 14 Sep 17 Responsibilities: o Supported senior engineers in designing ETL jobs using SQL Server Integration Services (SSIS) to extract and process data from multiple legacy systems. o Created scheduled data loads for operational reporting by writing T-SQL queries, stored procedures, and views in SQL Server. o Helped develop Python scripts for automating daily, weekly, and monthly data pulls from internal flat files and external APIs. o Maintained data flow from on-premise MySQL and Oracle databases to a centralized reporting system using SSIS packages. o Created lookup tables, constraints, and indexes to improve query performance and ensure data integrity. o Built basic dashboards and visualizations using Excel and Tableau to support internal operations reporting. o Conducted manual and automated data quality checks for key metrics, such as sales performance and SLA compliance. o Monitored job failures and prepared RCA (Root Cause Analysis) reports to aid in debugging and future prevention. o Supported a migration project moving flat-file-based reporting to relational models in SQL Server. o Wrote automation scripts in PowerShell and Python for file movement, cleanup, and data staging processes. o Helped maintain version control of scripts and job configurations using TFS (Team Foundation Server) and Git. o Gained hands-on exposure to basic concepts of data modeling, indexing, and performance tuning, laying the foundation for future growth. Environment: SSIS, SQL Server, T-SQL, MySQL, Oracle, Python, PowerShell, Excel, Tableau, Git, TFS, Agile. Keywords: continuous integration continuous deployment business intelligence sthree information technology New Jersey New York South Carolina Virginia