A bit more about me

I work on our enterprise Data Mesh platform team, focusing on the infrastructure and services that power analytics and customer facing SaaS workloads through Dremio.

• Deploy and operate Dremio lakehouse clusters across multiple Kubernetes environments using Helm and GitLab CI/CD.
• Maintain and improve Kubernetes infrastructure that supports enterprise analytics workloads.
• Implement and maintain SSO and OIDC based authorization for Dremio and Superset in collaboration with identity and security teams.
• Troubleshoot distributed system issues across Kubernetes networking, Hadoop/HDFS, Hive, Snowflake, and catalog services.
• Integrate secure APIs into the internal Data Mesh platform so users can run and retrieve queries through the UI.
• Migrate storage and catalog components during platform upgrades and improve Helm and Kubernetes configuration for maintainability.
• Improve platform observability by integrating Prometheus and Thanos metrics and expanding Grafana dashboards.
• Support data governance by improving Collibra domain structures and optimizing metadata sync jobs.
• Build Spark, Iceberg, and Polaris examples to support analytics and engineering use cases.
• Share platform knowledge through internal documentation and Data Mesh Guild walkthroughs.

Worked on the company's Data Mesh platform supporting governed data access, analytics enablement, and internal data products.

• Built a centralized data catalog that provides a unified view of enterprise data assets across teams.
• Designed the data mesh API layer and built MuleSoft proxy APIs over internal RPC services, collapsing a three-step auth flow into one authenticated REST call across dev, QA, and prod.
• Integrated internal web applications with Data Mesh services to simplify data discovery for users.
• Configured ETL pipelines supporting internal consumers and Snowflake Marketplace data products.
• Built and launched an internal Next.js application: the team's hub for Data Mesh activity and dataset metrics.
• Wrote internal documentation and engineering updates through Data Mesh Guild posts.
• Evaluated and deployed new technologies in Kubernetes and OpenStack environments to support architecture decisions.

Worked on enterprise data pipelines and analytics infrastructure supporting financial forecasting and modeling workloads.

• Served as technical lead for a team of five engineers from May to July 2022, leading design discussions, code reviews, and implementation planning.
• Built batch data processing pipelines using Domino, R, Python, Git CI/CD, and Airflow.
• Led the first team in the organization to migrate business-developed models to IT-supported infrastructure, cutting code by 60 percent and monthly operational effort by 95 percent, roughly $84K in annual savings per model.
• Developed ETL pipelines using Python, shell scripts, and IBM DataStage.
• Implemented runtime validation and monitoring to maintain data integrity across multiple pipeline stages.
• Orchestrated data workflows across Snowflake, Oracle, and Netezza databases.
• Built visualizations for financial forecasting data using React, D3, Plotly, and Tableau.

• Built data pipelines that moved application data into enterprise data warehouse platforms.
• Implemented validation controls to maintain data integrity across multiple stages of the pipeline.
• Developed ETL jobs using Python, shell scripts, and IBM DataStage.
• Taught quarterly internal training sessions on DataStage and ETL development practices.