V

Varsha Elnino Erri

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Michigan, United States

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Résumé

Jobs verified_user 0% verified
  • Sarnova
    Data Engineer II
    Sarnova
    Oct 2024 - Jul 2025 (10 months)
    Snowflake and Azure Synapse data models were developed and optimized using dimensional design and SCD logic, improving analytical query performance by 40% for sales and inventory reporting teams. Partnered with supply-chain and finance stakeholders to build Azure Data Factory pipelines that ingested Oracle ERP data into Azure Data Lake Gen2, increasing pipeline reliability and data availability by 35%. Designed PySpark transformation workflows in Azure Databricks to standardize multi-source order and inventory datasets, enabling 30% faster Power BI refresh cycles with improved data accuracy. Apache Kafka and Spark Structured Streaming pipelines were implemented to process 2M+ daily transactions, reducing operational reporting latency by 35%
  • Wells Fargo
    Data Engineer
    Wells Fargo
    Dec 2022 - Sep 2024 (1 year 10 months)
    Azure Data Factory, Databricks, and Snowflake pipelines were built to consolidate data from 12+ core banking systems, reducing manual reconciliation effort by 40% for finance and risk reporting teams. Improved large-scale batch and streaming workloads by tuning PySpark and Spark SQL jobs with partitioning and caching strategies, increasing processing efficiency by 30% and shortening reporting timelines. Real-time ingestion was enabled using Azure Event Hubs and Azure Functions, processing 1.5M+ transactions per day and lowering dashboard latency by 35% for operational users. Worked with governance and compliance teams to implement Azure Purview metadata and lineage tracking, which resulted in a 25% reduction in audit discrepancies during re
  • G
    Hadoop Developer
    GGK Technologies
    Aug 2019 - Jul 2021 (2 years)
    Apache Spark and Hive ETL pipelines were developed on Hadoop clusters to process structured and semi-structured datasets, reducing end-to-end processing time by 30% for analytics workloads. Designed enterprise ingestion workflows using Talend and Azure Data Factory to load data from 10+ source systems into HDFS and Snowflake, improving data availability for reporting teams. Hadoop clusters on AWS EC2 were monitored through Cloudera Manager with capacity planning activities, helping maintain 99% uptime across production environments. Implemented Kafka and Flume ingestion pipelines for high-volume event data, which reduced reporting latency by 35% and improved downstream data freshness. Orchestrated batch workflows using Oozie and Apache Airf
Education verified_user 0% verified
  • Trine University
    Masters in Information Science
    Trine University
    Jul 2025 - Current (1 year 3 months)
  • J
    Bachelor of Technology in Electrical & Electronics Engineering
    JNTU Hyderabad
    Aug 2017 - May 2021 (3 years 10 months)
Projects (professional or personal) verified_user 0% verified
  • R
    Real-Time Operational Analytics Platform
    Sep 2024 - Nov 2024 (3 months)
    Developed streaming pipelines using Kafka and Spark Streaming to process 1.5M+ transactions daily, enabling continuous ingestion for operational analytics use cases. Incremental data updates were handled through Delta Live Tables, which reduced analytics latency by 30% and kept reporting datasets consistently current. Implemented monitoring and alerting with Azure Monitor and Databricks jobs, improving pipeline reliability and enabling faster response to data processing failures.
  • E
    Enterprise ETL Automation & Cloud Migration
    May 2024 - Aug 2024 (4 months)
    Migrated legacy Oracle and SQL Server ETL workflows to Azure Data Factory and Databricks, modernizing batch pipelines and reducing overall processing time by 25%. Automated multi-source data transformations using Python and PySpark, increasing pipeline reliability by 35% and minimizing manual intervention during scheduled runs. Designed and implemented Snowflake schemas with CDC and SCD logic to ensure accurate, up-to-date datasets for finance and operations reporting.
  • A
    Azure-Snowflake Data Lakehouse Modernization
    Jan 2024 - Apr 2024 (4 months)
    Built a cloud lakehouse using Azure Data Factory, Databricks, and Snowflake to centralize data from multiple enterprise sources, improving data consistency for analytics and reporting teams. Applied Delta Lake Bronze-Silver-Gold architecture and Delta Live Tables to manage incremental processing and data quality, which reduced end-to-end data latency by 35%. Enabled near real-time reporting by developing streaming ingestion pipelines that processed 1M+ daily events, supporting timely Power BI dashboards for operational decision-making.