Data Architect

Data Architect Detroit/Charlotte onsite

About the Role

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)