Job Duties and Responsibilities

Candidates must be willing to participate in at least one in-person interview.

Modernizing and scaling enterprise data infrastructure requires sophisticated architecture that bridges traditional data modeling with contemporary lakehouse frameworks. This role resolves complex data fragmentation, storage tiering inefficiencies, and compute cost overruns across cloud environments to establish performant, enterprise-grade data assets. Crucially, this work unlocks advanced AI capabilities by designing architectures for unstructured data, vector databases, Retrieval-Augmented Generation (RAG), and semantic layers. Through cross-functional governance and technical leadership, enterprise data assets become securely managed, highly scalable, and fully aligned with strategic goals.

What Success Looks Like (Objectives):

• Establish and maintain corporate data model design principles; integrating Kimball and Inmon methodologies with Medallion Lakehouse patterns to deliver performant data assets
• Architect scalable frameworks and storage tiering to support AI initiatives; including vector databases, Retrieval-Augmented Generation (RAG), and enterprise semantic layers
• Lead efforts in optimizing query execution, cluster compute, and cloud warehouse costs across enterprise platforms
• Partner with enterprise architects, data engineering, and business analysis teams to enforce data governance standards and execute data SDLC phases
• Define data integration solutions and mitigate infrastructure risks to deliver scalable, highly available enterprise data architecture

Skills, Experience and Requirements

Core Skills and Competencies (What you'll bring):

• Advanced AI Application expertise; leveraging unstructured data frameworks, vector databases, and Retrieval-Augmented Generation (RAG) within modern data architectures
• Critical experience in designing multi-tier enterprise data structures, Medallion patterns, and high-performance data lakehouse architectures
• Strong skills in data modeling, schema design, and versioning within multi-modeler repository environments
• Critical experience in cloud warehouse cost optimization, cluster compute tuning, and query performance enhancement
• Expertise in data governance, fine-grained access control (ABAC), data lineage, and compliance standards such as CPNI, CCPA, and SOC2
• Exceptional problem solving, collaboration, and cross-functional decision-making capabilities across technical and business stakeholders

Additional Qualifications:

• Preferred industry certifications including Snowflake Certified Architect, Databricks Professional, or AWS Data Engineer

Minimum Requirements:

• Minimum Education: Bachelor's degree in a technical discipline, preferably in Computer Science or Software Engineering, or equivalent combination of education and experience
• Minimum Experience: 10 years of continuous data architecture and data modeling experience; with at least 3 recent years leading primary data architecture design and implementation in a VLDB / MPP environment
• Required Technical Skills:
• 2 years of experience with Azure, AWS, or GCP cloud environments
• 1 year of experience with Databricks & Genie and/or Snowflake & Cortex
• 1 year of experience with orchestration tools such as Apache Airflow or Control-M

Visa sponsorship not available for this position

Salary Ranges

Compensation: $127,050.00/Year - $181,500.00/Year
Benefits

We offer versatile health perks, including flexible spending accounts, HSA, a 401(k) Plan with company match, ESPP, career opportunities, and a flexible time away plan; all benefits can be viewed here: EchoStar Benefits.

The base pay range shown is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, and competencies; compensation is based on the role's location and is subject to change based on work location.

The posting will be active for a minimum of 3 days. The active posting will continue to extend by 3 days until the position is filled.