Data Architect

Data Architect Detroit/Charlotte onsite
Role Summary
As a Data Architect you'll design and own the data infrastructure that makes complex analytics and AI-powered client solutions possible. This is a hands-on technical role working at the center of our data practice   shaping how data flows, integrates, and performs across enterprise systems. The work you do here directly affects the quality of insights our clients rely on to make faster, smarter business decisions.
The Impact You'll Have
Data architecture  isn't a back-office function   it's the foundation that every analytics, AI, and customer experience solution is built on. When the data infrastructure is sound, everything downstream works better: campaign performance models are more accurate, personalization engines fire correctly, and clients get reporting they can actually trust.
You'll work on engagements where clients   often large B2B organizations in automotive, industrial, or enterprise technology sectors   are trying to connect disparate systems, improve data reliability, or modernize their pipelines to support cloud-native analytics. You'll partner closely with analytics, engineering, and strategy teams to make sure the architecture you design holds up in production and scales with the client's needs.
You'll also mentor junior data engineers and analysts, raising the technical bar across the team and helping others grow their craft alongside you.
What You'll Do
Design Enterprise Data Architecture
Build and implement scalable data architecture frameworks that support enterprise analytics and operational needs
Define data models, storage strategies, and integration patterns that translate business requirements into durable technical solutions
Ensure designs are built for performance, reliability, and long-term maintainability   not just to solve today's problem
Establish and Enforce Data Quality Standards
Develop data quality protocols that ensure consistency, accuracy, and reliability across systems and sources
Use strong SQL fundamentals to build validation and troubleshooting processes that catch issues before they reach end users
Set standards that analytics and engineering teams can operate against with confidence
Build and Optimize Data Pipelines
Lead integration work using APIs and modern pipeline approaches to connect systems that weren't designed to talk to each other
Optimize ETL/ELT workflows using Databricks and AWS-native services (Glue, Step Functions, Lambda) to build reliable, scalable pipelines
Leverage the Databricks Lakehouse platform   Delta Lake, Unity Catalog, and Spark-based processing   to improve pipeline efficiency and reduce operational overhead
Collaborate Across Engineering, Analytics, and Strategy
Work directly with engineering and analytics teams to align data architecture decisions with broader project goals and client outcomes
Partner with strategy and delivery teams to ensure data infrastructure supports the business use cases clients care most about
Translate technical architecture concepts for non-technical stakeholders clearly and without jargon
Develop Junior Data Talent
Provide technical guidance and mentorship to junior data engineers and analysts
Help build team-wide fluency in data architecture best practices, pipeline patterns, and data quality thinking
What You'll Need
Bachelor's degree in Computer Science, Information Systems, or a related field   or equivalent professional experience
5+ years of hands-on experience in data architecture development or implementation
Strong SQL skills across data analysis, validation, and troubleshooting
Hands-on experience with Databricks (Delta Lake, Unity Catalog, Spark) and AWS data services (Glue, Redshift, S3, Lambda, or Step Functions)
Familiarity with APIs and integration methods for connecting systems across an enterprise
Future-Ready Skills (Nice to Have)
Deep experience with AWS cloud data infrastructure (Redshift, S3, Glue, EMR, or similar) in a production environment, ideally alongside Databricks
Familiarity with Databricks MLflow or Feature Store as a bridge between data engineering and AI/ML workflows
Familiarity with marketing data ecosystems: CRM platforms, CDP architectures, or Martech/Adtech data flows
Experience in a digital agency, marketing services, or consulting environment where you've navigated multiple client data environments
Working knowledge of data governance or observability frameworks (e.g., data lineage, cataloging, or pipeline monitoring tools)