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Roilan Roman

@roilanroman5

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Staff Data Engineer building near real-time, reliable cloud data platforms.

United States

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What I'm looking for

I want to lead the build of reliable, near real-time data platforms—multi-region data lakes, CDC/streaming pipelines, and strong CI/CD—while improving data quality, integrity, and performance through pragmatic engineering.

I’m a Staff Data Engineer focused on delivering scalable, near real-time data access. At CVS Health, I achieved sub-10-minute latency for serving (Snowflake) and ingesting data (Kafka, Debezium) by building a multi-region data lake on AWS.

I also strengthened platform dependability by standardizing deployments with GitLab CI, Argo CD, and Terraform. I’ve built data integrity and replication approaches from CDC events, introduced CRISP-DM to improve documented data assets, and improved ETL/data quality using tools like Deequ; earlier roles delivered significant improvements through Spark/Delta pipelines, recommendation systems, and mentoring-driven test-driven development.

Experience

Work history, roles, and key accomplishments

Current

Lead Data Engineer

Current

CVS Health

Jun 2022 - Present (4 years)

Built a multi-region AWS data lake to achieve sub-10-minute latency for serving (Snowflake) and ingesting (Kafka, Debezium) data. Standardized deployments with GitLab CI, Argo CD, and Terraform, and improved data replication integrity and data asset processes using CDC validation and CRISP-DM.

AWS Snowflake Kafka Debezium Terraform Argo Gitlab CI Data Lake Crisp

Data Engineer

Databricks

Sep 2021 - May 2022 (8 months)

Reduced data/engineering issue tickets by 60% by introducing test-driven development and strengthening Spark (Scala) handling for semi-structured data. Improved data quality to achieve 100% accuracy using Deequ, increased mobile app engagement by 30% from 100+ A/B tests, and standardized ML deployment via BentoML CI/CD.

Test Driven Development Deequ Data Quality A B Testing BentoML CI CD Semi Structured Data

Data Engineer

Uber

Jan 2017 - May 2021 (4 years 4 months)

Reduced machine failures by 15% by ingesting and processing machinery data into monitoring dashboards, and generated an additional $5M in revenue by building a PySpark ML recommendation system feeding marketing via reverse ETL. Led platform improvements that cut support tickets by 35%, reduced bugs by 80% and lead time by 50% through pair programming and clean-code practices, and optimized legacy