S
Sai Yonas
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Emmett, Michigan, United States
Résumé
Jobs
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Staff Data EngineerCortexApr 2021 - Current (5 years 7 months)• Designed and optimized large scale healthcare data platforms supporting batch, streaming, and hybrid workloads using Python, PySpark, Kafka, and Airflow.
• Architected HIPAA compliant lakehouse and warehouse systems (Snowflake, BigQuery, Databricks, Delta Lake) enabling secure healthcare data integration and multi tenant isolation.
• Built scalable ETL/ELT pipelines processing complex healthcare datasets including HL7, DICOM, EMR, lab, and clinical records.
• Implemented end-to-end data governance, lineage, observability, and PII compliance frameworks across distributed cloud environments.
• Developed healthcare focused data models and semantic layers powering BI dashboards, predictive analytics, and AI/ML clinical ins- CSenior Data EngineerCambridge AnalyticaNov 2018 - Mar 2021 (2 years 5 months)• Built scalable SaaS data pipelines for multi-tenant analytics platforms using Python, SQL, Airflow, dbt, Kafka, and cloud services (AWS, GCP, Snowflake).
• Designed SaaS oriented data warehouses and lakehouse architectures supporting high velocity transactional and event driven data.
• Developed analytics-ready datasets powering SaaS product analytics, BI tools (Looker, Tableau, Power BI), and customer insights.
• Implemented CI/CD, data quality frameworks, and observability systems to ensure reliable SaaS data delivery.
• Built real time and batch pipelines supporting usage tracking, product metrics, and customer behavior analytics.
• Collaborated with product and engineering teams to convert SaaS requirements in - Data EngineerJawboneApr 2016 - Sep 2018 (2 years 6 months)• Developed secure and scalable data pipelines for financial and transactional data using Python, SQL, Airflow, dbt, and cloud platforms.
• Designed fintech grade data warehouses (BigQuery, Redshift, Snowflake) optimized for financial reporting, reconciliation, and analytics.
• Built real time streaming pipelines using Kafka and Pub/Sub for transaction processing and event monitoring.
• Created structured financial datasets supporting risk analytics, reporting dashboards, and business intelligence systems.
• Implemented data validation, monitoring, and compliance focused data quality frameworks.
• Worked closely with engineering and analytics teams to ensure accuracy and consistency of financial data flows.
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Education
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- Bachelors In Computer Sciencenot specified
- Apache Kafka Certified DeveloperConfluent
- dbt Fundamentalsdbt Labs
- Terraform AssociateHashiCorp
- Databricks Certified Data EngineerDatabricks
- AWS Certified Data AnalyticsAmazon Web Services
- Google Professional Data Engineer (GCP)Google
Projects (professional or personal)
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- PPersonalized Recommendation EngineDeveloped batch and real-time pipelines to support AI-driven product recommendations using Spark, Kafka, Airflow, and BigQuery. Led 9 engineers in building feature stores and improving pipeline reliability by 35%.
- MMulti-Tenant Data PlatformDesigned and implemented scalable pipelines for multi-tenant SaaS analytics, integrating API, event-stream, and log data into BI-ready datasets. Mentored 8 engineers on CI/CD pipelines, orchestration, and cloud architecture.
- PPatient Outcomes Data LakeCreated a centralized data lake for EMR, lab, and clinical data, applying NLP and OCR to extract structured insights for predictive modeling. Led 9 engineers on HIPAA-compliant pipelines and longitudinal patient-level datasets.
- RReal-Time Transaction Analytics PlatformBuilt and optimized real-time pipelines to monitor global transactions and prevent fraud using Python, PySpark, Kafka, Airflow, and BigQuery. Mentored 8 engineers while improving Spark job performance and pipeline reliability.