Open to opportunities

Tarsh Patel

@tarshpatel

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Senior Data Engineer specializing in cloud-scale ETL, real-time pipelines, and data platform optimization.

United States

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What I'm looking for

I seek a senior engineering role building scalable cloud data platforms and real-time pipelines, with opportunities to mentor, improve performance, and drive data-driven business outcomes.

I am a Senior Data Engineer with over 7 years' experience designing and optimizing large-scale, high-performance data systems across AWS, Azure, and GCP, delivering real-time and batch pipelines, lakehouse architectures, and ETL/ELT frameworks that produce measurable business value.

I have a proven record improving query and pipeline performance, reducing latency and costs, and deploying ML-driven solutions for trade surveillance and anomaly detection. I mentor junior engineers, lead cross-functional initiatives, and automate CI/CD and infrastructure to ensure reliable, scalable analytics platforms.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

JPMorgan Chase & Co

Oct 2022 - Present (3 years 8 months)

Optimized Snowflake queries and built real-time Kafka+Spark pipelines to reduce trading analytics latency from 10 minutes to under 30 seconds and trade settlement latency to <500ms, while standardizing 20+ KPIs via dbt and improving report consistency by 40%.

Snowflake Kafka Spark Structured Streaming DBT Pandas Apache Airflow Anomaly Detection

Data Engineer

Microsoft

Jan 2020 - Oct 2022 (2 years 9 months)

Architected a petabyte-scale Lakehouse with Azure Synapse, ADLS Gen2 and Databricks, boosting query performance by 65% and cut compute costs via T-SQL optimization; automated CI/CD and saved 25+ hours/week through PySpark ETL automation.

Azure Synapse Databricks Pyspark T SQL Terraform Jenkins Power BI

Data Engineer

Jan 2018 - Jan 2020 (2 years)

Built GCP-based pipelines with Cloud Composer, Dataflow and BigQuery to integrate 10+ sources, migrated legacy Hadoop to Dataproc/Spark reducing job runtimes by 50% and lowered processing time by 35% via serverless Beam.