Data Architect

Company Overview

We’re KSM, one of the top 50 largest independent advisory, tax, and audit firms in the United States. But more than our size, it’s our people and culture that set us apart. We believe great things happen when people are supported, challenged, and given the freedom to do their very best work. That’s why we’ve built a workplace where your career and personal life can thrive together – where you have the flexibility to manage your time, the resources to grow, and a team that genuinely cares about your success.

We’ve been employee-owned since 2001, giving every team member a stake in our collective success. This ownership mentality fosters a culture of collaboration, curiosity, and excellence – where new ideas are welcomed, different perspectives are valued, and you’re encouraged to explore what excites you most. Whether you’re expanding your skills, tackling new challenges, or making an impact beyond the office, we provide the resources and support to help you grow in the direction that’s right for you.

At KSM, your contributions matter – not just to the firm, but to your colleagues, clients, and the communities we serve across the U.S. If you’re looking for a place where you can do meaningful work, build lasting relationships, and grow in ways that align with what’s most important to you, we’d love to meet you.

Katz, Sapper & Miller (KSM) is seeking a hands‑on Data Architect to design, evolve, and operationalize the firm’s Databricks‑based lakehouse platform. This role is responsible for defining the architectural standards, governance models, and data design patterns that enable analytics, automation, and AI across the organization.

This position works in close partnership with the Senior Data Engineer, translating architectural vision into production‑ready solutions. The Data Architect focuses on consistency, scalability, and long‑term platform health, while remaining actively involved in implementation and validation.

This is not an ivory‑tower architecture role. The successful candidate will balance strategic thinking with hands‑on execution, ensuring that architectural decisions directly support reliable delivery and real business outcomes.

• Define and maintain KSM’s Databricks lakehouse architecture, including ingestion, storage, transformation, modeling, and access patterns.
• Establish clear, repeatable design standards for Bronze, Silver, and Gold data layers to ensure consistency and reuse.
• Design architecture that supports structured, semi‑structured, and unstructured data across enterprise systems.
• Design and implement data governance, lineage, and quality frameworks that ensure data is trusted, auditable, and scalable.
• Enable discoverability, access control, and metadata management using Databricks Unity Catalog and related tooling.
• Partner with data and analytics teams to define validation standards, reconciliation processes, and ownership models.
• Partner closely with the Senior Data Engineer to translate architectural standards into production‑ready pipelines.
• Review and validate data pipelines, models, and workflows to ensure alignment with architectural best practices.
• Support teams by providing guidance, patterns, and examples rather than one‑off solutions.
• Define best practices for Databricks compute and storage optimization, balancing performance and cost.
• Establish architectural patterns that promote reliability, scalability, and operational simplicity.
• Collaborate on monitoring, alerting, and operational standards to ensure platform health.
• Define and support CI/CD standards for data pipelines and lakehouse infrastructure.
• Partner with engineering teams to implement Infrastructure as Code (IaC) using tools such as Terraform.
• Ensure consistent, automated deployments across development, test, and production environments.
  
  Align data architecture decisions with firm‑wide analytics, automation, and AI strategy.
• Help prioritize architectural investments that deliver near‑term value while supporting long‑term growth.
• Evaluate emerging data platform capabilities thoughtfully, focusing on practical adoption rather than experimentation for its own sake.