Gnaneshwar Jasti

Sr. Data Engineer with 8+ years of experience designing and building end-to-end data pipelines, data warehouses, and analytics solutions across BFSI, CPG, Retail, and Energy domains, with experience in Snowflake, Oracle, SQL Server, AWS, Python, and modern cloud data platforms. Strong SQL expertise across Snowflake, Oracle, SQL Server, and PostgreSQL for data extraction, transformation, analysis, performance tuning, and reporting.

Experienced in conceptual, logical, and physical data modeling, including 3NF, Star Schema, dimensional modeling, fact and dimension design, surrogate keys, and handling slowly changing dimensions. Designed and maintained scalable data warehouse solutions that support business reporting, analytics, and operational data needs. Built ETL/ELT pipelines using Python, dbt, Data Factory, and Databricks to ingest, transform, and deliver data from multiple source systems.

Developed data ingestion and integration frameworks for structured and semi-structured data, ensuring reliable and consistent data movement across platforms. Worked with AWS services such as S3, EC2, RDS, and ECS to support data processing, storage, and application workloads in cloud environments.

Utilized

Python and SQL for data transformation, automation, scripting, and data quality validation across large datasets. Implemented data quality checks and validation processes to ensure data accuracy, consistency, completeness, and timely identification of anomalies. Developed CDC and SCD-based solutions to support historical tracking and maintain trusted reporting datasets.

Created interactive Power BI and Tableau dashboards that transformed complex datasets into meaningful insights for business stakeholders. Built reporting and semantic data models that enabled self-service analytics and standardized KPI reporting across business functions. Collaborated with business users, analysts, and engineering teams to gather requirements and deliver scalable data and reporting solutions.

Performed source-to-target mapping, data profiling, reconciliation, and testing activities to ensure data integrity and successful project delivery. Designed and deployed production-ready data pipelines across Snowflake, Oracle, Databricks, and cloud-based environments. Applied SQL, Spark, and platform-level performance tuning techniques to improve query efficiency, pipeline reliability, and reporting performance.

Supported CI/CD processes using GitHub, Jenkins, and Azure DevOps to streamline deployments and maintain code quality. Worked with workflow orchestration and scheduling tools to automate pipeline execution and manage dependencies across data workflows. Communicated technical solutions, data models, and project updates effectively with both technical and business stakeholders.

Hands-on GenAI experience: RAG pipeline design over structured/Snowflake data, LLM integration, vector embeddings, and natural-language analytics for business users. Experienced working in Agile Scrum envi

Sr. Data Engineer - JP Morgan & Chase - Plano, TX

(2025-11)

• Designed and developed scalable ETL/ELT pipelines using Python, PySpark, Databricks, SQL, and AWS services (S3, RDS) to ingest, transform, and process structured and semi-structured data for enterprise analytics and reporting.
• Designed a RAG pipeline over structured Snowflake datasets so analysts can ask business questions in plain English and get answers grounded in real data instead of writing SQL by hand.
• Worked with platform and data science teams to chunk and embed structured/tabular data for retrieval, since most RAG patterns are built for documents, not rows and columns.
• Leveraged AWS services including S3, EC2, RDS, and EKS for data storage, application hosting, and containerized workload support within hybrid cloud environments.
• Built and optimized data pipelines leveraging Delta Lake and CDC frameworks to support incremental data processing, historical tracking, and reliable downstream data consumption.
• Developed complex SQL solutions across Snowflake, Oracle, and SQL Server environments for data extraction, transformation, cleansing, reconciliation, and performance optimization.
• Designed and maintained dimensional and normalized data models, including Star Schema-based reporting structures, supporting enterprise data warehousing and analytical workloads.
• Implemented metadata-driven transformation frameworks and source-to-target mapping processes to improve data consistency, traceability, and maintainability.
• Built Python-based automation and API solutions to streamline data integration, workflow management, and data access processes across internal platforms.
• Applied data quality validation frameworks using SQL and PySpark to ensure data accuracy, completeness, consistency, referential integrity, and anomaly detection.
• Collaborated with business stakeholders, product owners, and architecture teams to gather requirements and deliver scalable data engineering solutions aligned with business objectives.
• Optimized large-scale Spark workloads using partitioning, caching, broadcast joins, and query tuning techniques to improve performance and reduce processing time.
• Implemented CI/CD pipelines using Git, GitHub, and Jenkins for automated deployment, testing, and version control of data engineering assets.
• Leveraged workflow orchestration and scheduling tools including Airflow and Control-M to manage pipeline dependencies, SLA monitoring, retries, and automated alerting.
• Created dashboards in Tableau and Power BI and reporting solutions to monitor data quality metrics, operational KPIs, workflow performance, and business reporting requirements.
• Worked within Agile Scrum teams, independently driving technical stories from design through deployment.

Data Engineer - PepsiCo - Plano, TX

(2023-05 - 2025-11)

• Led enterprise-wide data modeling initiatives, standardizing Star and Snowflake schemas with conformed dimensions across sales, retailer, POS, and trade promotion domains.
• Applied dimensional modeling methodologies (Kimball, Inmon, Data Vault) and tailored normalization/denormalization strategies to balance performance, scalability, and storage efficiency.
• Designed and optimized Azure Synapse, Snowflake, and Fabric warehouse environments, implementing partitioning, workload isolation, auto-scaling, and performance tuning strategies that improved query perf