Enterprise Data Architect

Principal Data \& AI Architect

Leap Metrics Richardson, Texas, United States (Hybrid)

Who We Are

Leap Metrics is a healthcare technology company headquartered in Richardson, Texas. We build Sevida, our AI-driven population health and care management platform, to help healthcare organizations improve health outcomes, lower the cost of care, and improve regulatory compliance.

Sevida means

“In the service of human lives.”

That idea reflects why we exist: to use technology to improve the lives of populations with complex and chronic healthcare needs.

We are a team of experienced technologists, developers, product leaders, designers, client advocates, and entrepreneurs who enjoy solving difficult problems and building meaningful software.

Why Leap Metrics?

At Leap Metrics, you will have the opportunity to solve complex technical problems while building technology with a meaningful purpose.

You won't simply inherit an architecture; you will help define what comes next.

Enhance the data foundation. Architect the intelligence. Improve human lives.

The Opportunity

We are looking for an exceptional

Principal Data \& AI Architect

to define and build the next generation of Sevida's data and AI platform.

This is a deeply hands-on architecture role. We are looking for a technical leader who loves to build, someone who can design a company-wide data architecture, personally prototype its most critical components, write production-quality Python and SQL, establish reference implementations and engineering patterns, and guide other engineers in building upon that foundation.

You will own architecture across the data lifecycle from ingestion and event streaming through transformation, storage, analytics, BI, and AI consumption.

Our modern data stack includes Kafka, Airflow, dbt, Snowflake, Python, SQL, cloud infrastructure, and BI technologies. You will help evolve this architecture as we build increasingly sophisticated AI and agentic capabilities into Sevida.

You should be equally comfortable designing a data platform, building an Airflow DAG, reviewing a dbt model, analyzing a Kafka pipeline, optimizing a Snowflake query, and prototyping how an AI agent securely accesses enterprise healthcare data.

If you love building complex systems and want the technical ownership to shape an entire platform, we would like to meet you.

Are you ready to build the backbone of the next generation of healthcare AI?

Why Work for Leap Metrics?

• MEANINGFUL MISSION – Empowering health providers to focus on care
• FULFILLMENT – Make an impact by building a meaningful product
• LEADING EDGE TECHNOLOGY – Advance your technical skills by working with the latest and most innovative technology.
• COMPANY CULTURE - team-oriented, collaborative, supportive, innovative

What You Will Own

Data \& Platform Architecture

• Define the long-term architecture and technical roadmap for Sevida's enterprise data and AI platform.
• Architect scalable data systems spanning ingestion, CDC, event streaming, orchestration, transformation, storage, analytics, BI, and AI workloads.
• Design and evolve enterprise data models, canonical models, metadata structures, data contracts, and semantic models.
• Design event-driven architectures using Apache Kafka,

including topics, schemas, partitioning strategies, consumer patterns, replay, error handling, and observability.

• Architect and optimize our Snowflake environment for scalability, performance, security, reliability, and cost.
• Design scalable transformation and orchestration architectures using DBT and Apache Airflow.
• Establish architecture patterns for batch, near-real-time, and real-time data processing.
• Define standards for data quality, lineage, observability, governance, lifecycle management, and reliability.

AI \& Agentic Architecture

• Define how enterprise data becomes a trusted, governed foundation for AI applications and agents.
• Architect how AI agents securely discover, retrieve, reason over, and act upon structured and unstructured enterprise data.
• Design and prototype AI architectures using technologies and patterns such as LLMs, RAG, embeddings, vector search, tool calling, MCP, agent orchestration, and AI evaluation frameworks.
• Establish patterns for grounding, context management, hallucination mitigation, evaluation, observability, guardrails, and human-in-the-loop workflows.
• Define secure AI data-access patterns that respect PHI, tenant boundaries, authorization, lineage, and audit requirements.
• Evaluate emerging AI technologies through working prototypes rather than relying solely on vendor claims or architectural diagrams.
• Collaborate with product, engineering, and data science teams to move AI capabilities from experimentation into reliable production systems.

Hands-On Engineering

• Personally prototype and build critical components of the architecture.
• Write and review Python, SQL, dbt models, Airflow workflows, APIs, and data-processing code.
• Build reference implementations and technical blueprints that other engineers can extend.
• Conduct architecture and code reviews for critical platform components.
• Troubleshoot difficult production problems across data pipelines, distributed systems, cloud infrastructure, and AI services.
• Use measurement, profiling, testing, and working prototypes to make architecture decisions.
• Establish engineering standards, design patterns, reusable components, and development practices.
• Continuously improve platform reliability, performance, scalability, maintainability, and cost.

Technical Leadership

• Translate complex product and business requirements into clear technical architectures and implementation blueprints.
• Make and document important architectural decisions and technical tradeoffs.
• Lead technical design discussions and contribute to Leap Metrics' architecture committee.
• Mentor engineers and raise the technical bar across the organization.
• Partner closely with engineering, product, data science, BI, security, and executive leadership.
• Help recruit and develop exceptional engineers.
• Evaluate technologies and vendors when appropriate while maintaining strong architectural ownership internally.

What We're Looking For

• 7\+ years of progressively advanced software, data, or platform engineering experience, including significant architecture responsibility.
• Bachelor's or Master's degree in Computer Science, Computer Engineering, Software Engineering, Data Science, or a closely related technical discipline, or equivalent demonstrated technical depth.
• Proven experience architecting and personally building large-scale production data platforms.
• Deep hands-on expertise with Apache Kafka, Snowflake, dbt, Apache Airflow, Python, and SQL.
• Strong understanding of distributed systems, event-driven architecture, data pipelines, APIs, and cloud-native architecture.
• Deep experience with enterprise data modeling, analytical data models, data warehouses, and modern data-platform patterns.
• Experience designing highly reliable ETL/ELT and streaming pipelines.
• Strong understanding of data contracts, schema evolution, metadata, lineage, data quality, observability, and governance.
• Experience with Docker, Kubernetes, infrastructure-as-code, CI/CD, and modern cloud environments such as AWS or GCP.
• Demonstrated ability to diagnose difficult performance, scalability, reliability, and cost problems.
• Strong software-engineering fundamentals—not just experience configuring data products.
• A track record of creating architecture that other engineers successfully implement and extend.
• Demonstrated curiosity and ability to rapidly learn, prototype, evaluate, and apply emerging technologies.

AI Experience

We are particularly interested in experience with several of the following:

• Large Language Models (LLMs)
• Agentic AI and agent orchestration
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector search
• MCP and tool/function calling
• Structured and unstructured data retrieval
• AI evaluation and observability
• Model and prompt evaluation
• Guardrails and human-in-the-loop architectures
• AI security and authorization
• Production AI application architecture

We value engineers who experiment and build with AI themselves, not simply those who have managed AI initiatives.

Healthcare Experience — Preferred

Healthcare experience is valuable but not required. Experience with any of the following is a plus:

• Healthcare claims and clinical data
• HL7 and FHIR
• X12/EDI
• Population health or care management
• HIPAA and PHI
• Healthcare interoperability
• Healthcare analytics

Location

US only - Hybrid - Dallas/Ft. Worth Metroplex; commutable distance to Richardson, Texas

Compensation \& Benefits

• Competitive salary (commensurate with experience).
• Comprehensive benefits package including Health, Dental and Vision insurance, and 401(K)

How to Apply

If you are passionate about healthcare technology and excited to make a real impact, we’d love to hear from you!