Teams at Dynata

Designs, builds, and maintains scalable batch and streaming data pipelines, lakehouse medallion architectures, transformation layers, and data models. Develops PySpark and SQL solutions, integrates diverse data sources, implements data quality monitoring, and manages infrastructure as code. Troubleshoots pipeline and data incidents, participates in code reviews, collaborates with architects, and mentors junior and mid-level engineers.

Owns the lifecycle and roadmap of data products on a lakehouse platform. Defines requirements, schemas, SLAs, access controls, and documentation; collaborates with data engineers on delivery; tracks adoption, quality, and business value KPIs; manages catalogs and metadata; leads sprint planning, backlog grooming, capacity, and releases; and coordinates governance, security, and compliance policies.

Leads enterprise semantic layer, data, and AI platform architecture. Defines lakehouse, medallion, data model, data contract, metadata, schema governance, feature store, model lifecycle, and interoperability standards. Partners with product, engineering, platform, and data science teams to create secure, reusable foundations for analytics, machine learning, and AI applications. Translates business and governance requirements into scalable technical architectures and provides thought leadership across enterprise data and AI enablement.