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HMG America LLC is seeking a Data Architect to own the design and delivery of modern data platforms built on AWS and Databricks. The role blends hands-on engineering with team leadership, spanning data ingestion, transformation, and warehousing across structured and unstructured sources.
Location is Seattle, WA / Dallas, TX / Phoenix, AZ / Charleston, SC with a Hybrid model (1-2 days per week). You will lead data engineers, set standards for data quality, governance, and platform scalability,
HMG America LLC is the best Business Solutions focused Information Technology Company with IT consulting and services, software and web development, staff augmentation and other professional services. One of our direct clients is looking for Data Architect (AWS Databricks) in Seattle, WA / Dallas, TX / Phoenix, AZ / Charleston, SC. Below is the detailed job description.
We are looking for a Data Architect to own the design and delivery of modern data platforms built on AWS and Databricks. This role combines deep hands‑on engineering with team leadership, covering data ingestion, transformation, and warehousing across structured and unstructured sources.
AWS Services Hands-on expertise across core AWS services spanning compute, storage, orchestration, and security in production environments.
Databricks Platform Deep proficiency in Databricks, including Delta Lake, Unity Catalog, cluster management, and job orchestration at scale.
Data Engineering Lifecycle Proven ability to design and operate ingestion, transformation, and warehousing pipelines across structured and unstructured data.
Team Leadership Experience leading and mentoring data engineering teams, managing delivery timelines, and driving technical quality.
Agentic AI Exposure Working understanding of agentic AI concepts and frameworks, and how they apply within modern data platforms.
Manufacturing Industry Knowledge Familiarity with manufacturing industry data landscapes, processes, and common use cases.
Data Modelling Understanding of data modelling principles, including dimensional and canonical modelling approaches.