Pranavesh V

• Designed and optimized complex Snowflake and Oracle SQL transformations, stored procedures, views, and analytical queries supporting large-scale financial performance measurement and institutional wealth analytics platforms across AWS and Azure environments. • Built and maintained high-performance Snowflake ELT pipelines using Streams, Tasks, Snowpipe, clustering strategies, and virtual warehouse optimization. Integrated Ab Initio and IDMC (Informatica Intelligent Data Management Cloud) workflows to orchestrate enterprise data ingestion and transformation processes. • Developed scalable ETL solutions using Ab Initio graphs and CDC frameworks, implementing incremental data loading, MERGE operations, window functions, and CTE-based transformations to ensure accurate and high-throughput data processing. • Integrated Snowflake with AWS (S3, Redshift) and Azure (ADLS, Data Factory) services to support hybrid cloud data warehouse architectures, optimizing storage, compute performance, and cost efficiency. • Implemented data quality validation frameworks, automated testing strategies, and production monitoring across Snowflake, Ab Initio, and IDMC environments, ensuring SLA adherence and regulatory compliance. • Performed SQL query tuning, execution plan analysis, indexing strategies, and warehouse sizing optimizations to improve performance across high-volume financial datasets. • Collaborated with Java application and API teams to integrate database transformation layers with backend systems, delivering secure, scalable, and maintainable data services. • Actively participated in Agile Scrum ceremonies, mentored junior developers, enforced coding standards, and contributed to reusable ETL component design and operational runbook documentation.

Designed and developed Snowflake-based data warehouse solutions for healthcare analytics, implementing advanced SQL transformations, stored procedures, MERGE logic, clustering strategies, and incremental loading frameworks to support large-scale analytical workloads. • Built and optimized complex SQL queries (CTEs, window functions, aggregations, execution plan tuning) across Snowflake, Oracle, and SQL Server environments to support customer analytics, operational reporting, and regulatory data processing. • Led Azure-to-Snowflake migrations by performing detailed source-to-target mapping, schema redesign, T-SQL refactoring, and Snowflake performance tuning, ensuring zero data loss through reconciliation and audit validation frameworks. • Developed CDC-based data ingestion pipelines using Snowflake Streams & Tasks and automated validation frameworks to maintain data consistency, data lineage tracking, and SLA-driven processing in regulated PHI-compliant environments. • Implemented query optimization strategies including partition pruning, clustering key adjustments, warehouse sizing, and cost-performance balancing to improve data processing efficiency across high-volume healthcare datasets. • Collaborated with data scientists, application developers, and business stakeholders within Agile Scrum teams to translate analytical requirements into scalable SQL-driven data models, ensuring secure and compliant healthcare data handling.

•Designed the business requirement collection approach based on the project scope and SDLC methodology. •Installed, configured and maintained Data Pipelines, Developed Data Pipeline with Kafka and Spark. •Worked in building application platforms in the Cloud by leveraging Azure Databricks and created Hive External tables to stage data and then move the data from Staging to main tables. Architected and maintained a reusable data-platform framework covering monitoring, CI/CD pipelines (GitHub Actions, Jenkins), automated testing, scalability tuning, resiliency and observability for ETL/ELT workloads. •Creating, scheduling, and monitoring Azure Data Factory pipelines and Spark jobs on aw SQL •Transforming business problems into Big Data solutions and defining Big Data strategy and Roadmap. •Authored Python (PySpark) Scripts for custom UDF's for Row/ Column manipulations, merges, aggregations, stacking, data labeling, DataStage and for all Cleaning and conforming tasks. •Evaluated Snowflake Design considerations for any change in the application and built the Logical and Physical data model for snowflake as per the changes required. •Redesigned the Views in snowflake to increase the performance and Unit tested the data between and Snowflake. •Created and deployed data dashboards on docker to visualize the inquiries querying in SQL to retrieve data as per client inquiries providing daily reports using production data and experimental analysis using development data. •Developed data warehouse model in snowflake for over 100 datasets using where Scape and Created Reports in Looker based on Snowflake Connections. •Written Pig Scripts to generate Map Reduce jobs and performed ETL procedures on the data in HDFS. •Developed solutions to leverage ETL tools and identify opportunities for process improvements using Informatica and Python. •Performed advanced procedures like text analytics and processing, using the in-memory computing capabilities of Spark using Scala.

•Developed PySpark Data Ingestion framework to ingest source data into HIVE tables by performing Data cleansing, Aggregations and applying De-dup logic to identify updated and latest records. •Involved in creating End-to-End data pipeline within distributed environment using the big data tools, Spark framework and Tableau for data visualization. •Experience in creating python topology script to generate cloud formation template for creating the EMR cluster in AWS. •Experience in using the AWS services Athena, Redshift and Glue ETL jobs. •Designed and created tabular models on Azure Synapse Analytics to meet business reporting requirements and enhance data analysis capabilities. •Orchestrated data ingestion into Azure cloud, including Azure Data Lake, Azure Storage, Azure SQL, and Azure Synapse Analytics (Azure SQL DW), while migrating data using Azure Databricks. •Developed end-to-end data pipelines, data flows, and complex data transformations using Azure Data Factory (ADF) and PySpark. •Utilized Azure Data Factory (ADF) pipelines and triggers to schedule and automate data ingestion, transformation, andprocessing tasks. Acted as senior developer, reviewed code across multiple teams, enforced high code quality, performance best practices, and guided junior engineers in Spark-based ETL and cloud-native data pipelines. •Utilized Azure Blob and Data Lake storage to load and manage data within Azure Synapse Analytics (Azure SQL DW). •Employed Python, PySpark, and Bash scripts to facilitate seamless data transformation and loading across hybrid on-premises and cloud platforms. •Led senior development activities: gathering and refining business requirements, designing solution architecture, reviewing code, mentoring team members, and driving delivery of production‐ready services on schedule and budget. •Utilized Apache Spark's capabilities, including Spark SQL and Streaming components, for real-time data processing needs.

• Developed Custom UDF in Python and used UDFs for sorting and preparing the data. • Used HIVE to do transformations, event joins and some pre-aggregations before storing the data onto HDFS. • Used Spark-SQL to load JSON data and create schema RDD and loaded it into Hive tables handled structured data using Spark SQL. • Developed data pipeline using flume, Sqoop, pig and map reduce to ingest customer behavioral data and purchase histories into HDFS for analysis. • Used Apache Spark Streaming to process and analyze real-time data streams, enabling near real-time insights and actions. • Developed the applications on the data lake to transform the data according to business users to perform analytics. • Written Map Reduce code that will take input as log files and parse them and structures them in a tabular format to facilitate effective querying on the log data. • Implemented fair schedulers on the Job Tracker to share the resources of the cluster for the Map Reduce jobs given by the users. • Involved in developing a Map Reduce framework that filters bad and unnecessary records. • The Hive tables created as per requirement were internal or external tables defined with appropriate static and dynamic partitions, intended for efficiency. • Implemented the workflows using Apache Oozie framework to automate tasks. • Played a key role in setting up the CI/CD pipeline using Jenkins, Maven, Nexus, GitHub, and Docker for efficient application deployment and containerization. • Maintained operational support of live data pipelines: implemented monitoring dashboards (CloudWatch / Databricks metrics), alerts for failures, performance-tuning of Spark jobs, automatic retries and SLA-governed ingestion windows.