Data Engineer
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Data Engineer experienced in designing and building scalable, cloud-native data platforms on AWS and Azure.
My expertise includes ETL/ELT and CDC pipelines using PySpark, Databricks, AWS Glue, and DBT, as well as implementing data warehouses and lakes in Redshift, Snowflake, and Azure Data Lake/ADF. I focus on delivering high-performing, secure, and cost-efficient solutions, leveraging Terraform, CI/CD, and containerization (Docker/Kubernetes) to build reproducible infrastructure at scale.
💡 Key Highlights
Designed and automated pipelines to ingest, transform, and model large-scale datasets across AWS & Azure.
Optimized Spark/Databricks jobs for performance and cost efficiency.
Built analytics-ready repositories in Redshift, Snowflake, and Databricks with governance and traceability.
Partnered with data scientists, BI analysts, and engineers to deliver clean, reliable, and analytics-ready data.
Implemented security, access controls, and compliance standards (HIPAA, GDPR).
Experienced Data Engineer with over 5 years of expertise in designing and managing large-scale data infrastructure and analytics solutions across AWS, Snowflake, and Redshift ecosystems. Proven track record in building ETL/ELT pipelines, optimizing data workflows, and automating data ingestion using Python, PySpark, and SQL. Skilled in containerization (EKS, Docker), workflow orchestration (Airflow), and infrastructure as code (Terraform, Jenkins).
Strong focus on data governance, scalability, and compliance (HIPAA/GDPR).
Master’s in Computer Science — University of the Pacific (Aug 2023 – Dec 2024)
Focused on cloud computing, data engineering, and applied machine learning, with coursework in web applications, security, and data visualization.
Built a strong foundation in digital systems, embedded systems, and automation, with hands-on experience in sensor integration and control systems.
