Data Architect

KSM•Indianapolis, IN

21d

About The Position

Katz, Sapper & Miller (KSM) is transforming into a data-driven, AI-empowered firm. The Data Architect will lead the design and implementation of KSM’s enterprise data lakehouse on Databricks, enabling analytics, automation, and AI across the organization. This role combines strategic vision and hands-on technical execution, ensuring data is modeled, governed, and leveraged as a strategic asset.

Requirements

7+ years of experience in data architecture, data engineering, or enterprise data design.
Proven experience designing Databricks or other modern lakehouse architectures
Deep expertise in data modeling, ETL/ELT design, and governance frameworks
Strong understanding of modern data architecture patterns, including medallion architecture, data mesh, and real-time streaming.
Proficiency in SQL, Python, PySpark, and orchestration tools (e.g., Airflow, dbt, or similar).
Experience implementing CI/CD and infrastructure-as-code for data platforms using Terraform
Familiarity with Power BI or similar BI/analytics tools.
Demonstrated ability to collaborate effectively across technical and business teams to deliver measurable results.
Excellent communication and documentation skills with the ability to explain complex technical concepts clearly.

Nice To Haves

Experience implementing Unity Catalog and Delta Sharing for governed data access.
Exposure to machine learning and AI integration within data pipelines.
Background in professional services or multi-entity organizations.
Certifications in Databricks or Data Engineering.

Responsibilities

Design KSM’s Databricks-based lakehouse architecture, integrating structured, semi-structured, and unstructured data from diverse enterprise systems.
Define the end-to-end data architecture blueprint, covering ingestion, storage, modeling, governance, and access.
Develop and implement data governance, lineage, and quality frameworks that ensure scalability, compliance, and trust.
Create and maintain enterprise data models (conceptual, logical, and physical) to support analytics, BI, and AI use cases.
Collaborate with data teams to design high-performance ETL/ELT pipelines, Delta Live Tables, and streaming data processes.
Optimize Databricks compute and storage performance, applying cost and performance best practices.
Implement CI/CD standards for data pipelines and infrastructure-as-code to ensure consistent, automated deployments.
Enable discoverability and access through effective metadata management and Unity Catalog.
Partner with business and IT stakeholders to align data architecture decisions with firm-wide analytics and AI strategy.
Evaluate emerging technologies in data platforms, AI integration, and cloud infrastructure to guide KSM’s data evolution.