Open to opportunities

Shaun K

@shaunk

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Senior Data Engineer specializing in cloud-native big data, ETL, and real-time analytics.

United States

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What I'm looking for

I seek roles building scalable cloud data platforms and real-time pipelines, with strong team collaboration, automated CI/CD/IaC, and a focus on compliance and impactful analytics.

I am a Senior Data Engineer with 9+ years delivering cloud-native, scalable data platforms across healthcare, finance, and manufacturing. I design and implement end-to-end ETL/ELT pipelines, real-time ingestion, and data warehouses using tools such as Airflow, dbt, Talend, Informatica, Snowflake, and Databricks.

I built a serverless pipeline processing 500M+ daily events with 99.99% uptime, reduced data latency by 45%, and improved reporting speed and accuracy through Data Vault and Kimball modeling. I drive data governance and compliance (HIPAA, GDPR, SOC2), automate data quality, and deploy ML models with MLflow, TensorFlow, and scikit-learn.

I lead teams to deliver production-ready analytics, automate CI/CD and IaC with GitLab, Terraform, Docker, and Kubernetes, and create BI visualizations with Tableau and Power BI to enable data-driven decisions and measurable operational gains.

Experience

Work history, roles, and key accomplishments

Current

Lead Data Engineer

Current

Lark Health

Jan 2022 - Present (3 years 10 months)

Built cloud-native data pipelines for EHR and claims integration, reducing data latency by 45% and improving reporting speed; automated data quality across 25 datasets and deployed ML readmission models with CI/CD.

Airflow AWS Glue Talend Kafka Snowflake Tableau Python CI CD

Senior Data Engineer

EE-Medix

Aug 2018 - Dec 2021 (3 years 4 months)

Designed enterprise financial data warehouse and migrated legacy systems to cloud platforms, cutting reporting time by 50% and infra costs by 35% while ensuring SOC2/GDPR compliance.

Azure Synapse BigQuery Informatica DBT Kafka Streams Power BI

ETL & Data Warehouse Engineer

Azumo

Jun 2015 - Jul 2018 (3 years 1 month)

Built scalable big data platforms processing 15+ TB of sensor data and enabled real-time analytics, improving insight velocity by 40% and detecting 90% of anomalies via deep learning pipelines.