Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I’m a Data Engineer with experience building and operating reliable batch and near-real-time data pipelines in healthcare and retail. I specialize in SQL, Python, Snowflake, and cloud platforms on AWS and Azure, with a strong focus on data quality, CDC, and analytics-ready data models. I’ve spent much of my career supporting production systems in regulated environments, owning pipelines end to end and ensuring secure, accurate data delivery for business and compliance reporting.…I’m a Data Engineer with experience building and operating reliable batch and near-real-time data pipelines in healthcare and retail. I specialize in SQL, Python, Snowflake, and cloud platforms on AWS and Azure, with a strong focus on data quality, CDC, and analytics-ready data models. I’ve spent much of my career supporting production systems in regulated environments, owning pipelines end to end and ensuring secure, accurate data delivery for business and compliance reporting.

Sai K





I’m a Data Engineer with experience building and operating reliable batch and near-real-time data pipelines in healthcare and retail. I specialize in SQL, Python, Snowflake, and cloud platforms on AWS and Azure, with a strong focus on data quality, CDC, and analytics-ready data models. I’ve spent much of my career supporting production systems in regulated environments, owning pipelines end to end and ensuring secure, accurate data delivery for business and compliance reporting.…I’m a Data Engineer with experience building and operating reliable batch and near-real-time data pipelines in healthcare and retail. I specialize in SQL, Python, Snowflake, and cloud platforms on AWS and Azure, with a strong focus on data quality, CDC, and analytics-ready data models. I’ve spent much of my career supporting production systems in regulated environments, owning pipelines end to end and ensuring secure, accurate data delivery for business and compliance reporting.

Available to hire

I’m a Data Engineer with experience building and operating reliable batch and near-real-time data pipelines in healthcare and retail. I specialize in SQL, Python, Snowflake, and cloud platforms on AWS and Azure, with a strong focus on data quality, CDC, and analytics-ready data models. I’ve spent much of my career supporting production systems in regulated environments, owning pipelines end to end and ensuring secure, accurate data delivery for business and compliance reporting.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Language

English

Advanced

Work Experience

Data Engineer at Walgreens

May 1, 2024 - Present

Currently engineering and maintaining high-volume batch ETL workflows ingesting 1M+ records per week, ensuring reliable transformation, validation, and delivery of healthcare and benefits data to downstream systems. Architected cloud-based data storage and processing using Snowflake and Amazon S3. Orchestrated batch data pipelines with AWS Glue Workflows and Apache Airflow, implementing retries, SLA monitoring, and failure alerting for regulated healthcare datasets. Applied incremental load and CDC patterns to claims and eligibility pipelines to improve data freshness while minimizing reprocessing. Built Python and SQL-based data quality, validation, and reconciliation frameworks for healthcare claims and eligibility data, reducing downstream issues. Developed analytics-ready data models in Snowflake and published governed datasets to Power BI for actuarial, finance, and compliance reporting. Led production support and incident response for healthcare data pipelines, diagnosing upstrea

Data Engineer at Target

May 1, 2023 - April 1, 2024

Designed and owned cloud-based, large-scale retail data pipelines on AWS (S3, EC2) and Snowflake for enterprise analytics. Built SQL and Python-driven ETL workflows to ingest batch and near real-time data from transactional systems and flat-file sources, supporting millions of records with high reliability. Built and supported event-driven ingestion pipelines using Apache Kafka to consume near real-time retail transaction and inventory events. Optimized Spark transformations on AWS Glue and EMR to meet SLA during peak retail traffic. Implemented incremental and CDC ingestion patterns to load retail transactional data into Snowflake. Developed dimensional and analytical data models to support merchandising, finance, and supply-chain reporting. Created self-service data marts and curated datasets enabling analysts to access trusted retail data, reducing ad-hoc requests by 30%. Enforced data quality, governance, security, and RBAC for sensitive retail and financial data.

Data Engineer at Flipkart

January 1, 2021 - June 1, 2022

Owned production ingestion pipelines for high-volume retail transaction and financial data in a large-scale e-commerce environment. Engineered real-time and batch data processing to support order lifecycle monitoring and financial reconciliation. Designed AWS-based data lake architecture supporting raw, curated, and analytics-ready datasets for retail and finance data at scale. Supported near real-time ingestion using Amazon Kinesis to process order and payment events for downstream analytics. Implemented automated ingestion and transformation frameworks for vendor, marketplace partner and third-party flat-file feeds, ensuring consistent data normalization across retail systems. Developed data validation, reconciliation, and anomaly-detection logic to improve data accuracy, reducing discrepancies by 20%.

Data ETL Engineer at HCL

September 1, 2018 - November 1, 2020

Implemented ETL workflows using SSIS and Talend to ingest, cleanse, and standardize large-scale vendor and internal datasets, improving data accuracy and consistency. Designed data integration solutions connecting marketing platforms, web analytics sources, and internal databases to support unified customer and revenue data views. Developed scalable data transformation frameworks using Python and SQL to process vendor feeds, enforce schema consistency, and validate data integrity. Optimized SQL queries and transformations for high-usage reporting workloads. Supported data ingestion and transformation on Azure Data Lake using Python and SQL, enabling centralized storage and analytics access for enterprise sales and customer datasets.