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supriya User

@supriyauser1

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Senior Data Engineer specializing in Azure cloud, ETL/ELT pipelines, and real-time data solutions.

United States

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What I'm looking for

I seek a senior data engineering role building secure, scalable cloud-native data platforms (Azure preferred) enabling analytics and ML, with opportunities to mentor and lead architecture.

I am a Senior Data Engineer with 12+ years building end-to-end ETL/ELT pipelines, data lakes, and enterprise data warehouses primarily on Microsoft Azure.

I design scalable batch and streaming workflows using Azure Data Factory, Synapse, Databricks, Event Hubs, Kafka, and Spark to deliver reliable analytics and real-time data access for business stakeholders.

I drive performance and cost optimization through partitioning, caching, Delta Lake, and query tuning, and I have integrated Snowflake, DBT, and CI/CD practices using Azure DevOps, Terraform, and Docker/Kubernetes.

I prioritize security and compliance (HIPAA, GDPR, PCI-DSS, ISO 27001), mentor junior engineers, and collaborate with data science teams to enable ML and Generative AI capabilities including Azure ML and OpenAI integrations.

Experience

Work history, roles, and key accomplishments

Current

Senior Azure Data Engineer

Current

Cigna Healthcare

Mar 2023 - Present (2 years 7 months)

Designed and implemented end-to-end Azure data integration and streaming pipelines for healthcare data, improving data availability by 35% and achieving 40% faster delivery while reducing compute costs by 25%.

Azure Data Factory Databricks Python Pyspark Delta Lake Kafka SQL Terraform

Data Engineer

City Union Bank

May 2018 - Jul 2022 (4 years 2 months)

Migrated on-prem ETL to Spark/Hadoop, built reusable PySpark templates and optimized jobs to reduce processing time by 30–40% while implementing monitoring and data quality validations.

Pyspark Hadoop HDFS Hive Databricks Parquet Oozie Airflow Jenkins Git

Data Analyst / Data Engineer

Capgemini

Sep 2009 - Dec 2015 (6 years 3 months)

Designed and maintained batch pipelines for large-scale financial datasets, automating multi-source integrations and reducing manual processing time by ~30% to support reporting and regulatory compliance.