Reshma Shaik

Data Engineer with 3+ years of experience building and maintaining high-performance data pipelines within healthcare and fintech environments. Expert in developing ELT processes using Python, PySpark, and AWS Glue to populate cloud data warehouses like Snowflake and Amazon Redshift. Proficient in writing complex SQL for data validation and optimizing workflows through Apache Airflow and AWS Lambda.

Focused on ensuring data integrity with Great Expectations and delivering actionable insights via Power BI and MicroStrategy. Adept at managing end-to-end data lifecycles while maintaining high availability through proactive monitoring and automation to support critical business operations.

Data Engineer at Eucalyptus (2025-01 – Present)

• Built and maintained critical data pipelines for the telehealth platform using Python and Apache Airflow to ensure consistent data delivery for patient healthcare tracking and reporting.
• Developed well-modeled datasets for clinical reporting and experimentation by utilizing SQL and DBT to provide structured information for internal analysts and data scientists.
• Designed new data pipelines for user segmentation features by implementing real-time data processing with Apache Kafka to support personalized digital healthcare experiences.
• Improved the efficiency of existing business data pipelines by optimizing ETL jobs with Apache Spark to provide faster and more reliable data delivery across the platform.
• Created robust data quality checks and monitoring systems for critical healthcare datasets using Great Expectations to maintain high data integrity for patient record management.
• Developed the Zendesk data platform for enhanced customer support analytics by integrating interaction data to help the support team gain actionable insights.
• Implemented data deletion processes adhering to privacy regulations by developing secure data scrubbing routines within the core data platform to protect sensitive user information.
• Built bespoke streaming data solutions for real-time reporting by utilizing AWS Kinesis and Lambda to provide dynamic insights into clinic operations and patient engagement.
• Supported data governance initiatives by documenting data flows and schemas using Atlan to manage metadata and improve transparency for the engineering and analytics teams.
• Integrated specific regional requirements within the AWS data lake by working with global teams to implement standardized data formats and reliable storage practices.

Data Engineer at Marshmallow (2022-03 – 2023-05)

• Developed data pipelines using Python and AWS Glue to ingest diverse customer application and policy data from various sources into S3 for centralized storage and processing.
• Implemented data quality checks using SQL and Python scripts, maintaining version control with Git to ensure accuracy and consistency of crucial customer and policy datasets in the lake.
• Configured and managed data workflows using Apache Airflow, automating daily data refreshes for Power BI and Strategy dashboards to support real-time business decision-making for retail analytics.
• Automated data loading and transformation tasks for new insurance products using Python and SQL, reducing manual effort significantly while improving the reliability of the ingestion process.
• Built ETL processes with Apache Spark on AWS EMR to transform raw customer interaction data for analytics and reporting purposes, enhancing the speed of downstream data processing.
• Optimized SQL queries and Redshift clusters, improving query performance for various business users accessing insurance and payment data for critical financial analysis and reporting.
• Created and maintained a robust data warehouse schema in Redshift, enabling deep analysis of international customer financial product usage patterns and historical policy performance.
• Maintained monitoring solutions with AWS CloudWatch for data pipelines, proactively identifying and resolving issues to ensure data integrity and system availability for the analytics teams.
• Designed and implemented data models in Amazon Redshift, supporting critical business intelligence needs for fraud detection and pricing optimization within the insurance domain.
• Delivered well-structured datasets from the Redshift data warehouse to support analytics teams in performing deep dives using MicroStrategy to analyze customer behavior and transactional patterns.

MSc in Data Science – Coventry University