Data Architect – Customer Portfolio (Data Products & Lakehouse Architecture)

Expleo•Denver, CO

5d•$97 - $107•Hybrid

About The Position

Trissential is hiring a Data Architect for a client’s Customer Portfolio team to support large-scale customer data modernization initiatives. The role focuses on Customer 360, enterprise data platforms, AI enablement, and CIS modernization, using technologies including AWS and Databricks. The position is hybrid in Denver, Colorado, or Minneapolis/St. Paul, Minnesota, and is a contract opportunity with a high likelihood of extension. The Data Architect will design customer-domain data products and scalable data architectures, including end-to-end pipelines into a Databricks Lakehouse hosted on AWS. They will define integration patterns and data access strategies, establish system-of-record and system-of-truth standards, and guide data migration and modernization architecture. The role also provides architectural leadership, maintains architecture documentation, and supports AI readiness and advanced analytics initiatives in collaboration with architects, engineers, governance teams, business stakeholders, and vendors.

Requirements

At least 8 years of experience in Data Architecture, Enterprise Architecture, or related data-focused roles.
Strong AWS data architecture experience across cloud-native data services and integration platforms.
Hands-on expertise designing and implementing Databricks Lakehouse solutions.
Deep understanding of data product design principles and domain-driven architecture concepts.
Advanced data modeling experience, including conceptual, logical, and physical data modeling techniques.
Experience defining and implementing modern data integration patterns, including ELT, CDC, event-driven architectures, and streaming technologies.
Experience working across hybrid cloud, on-premises, and SaaS application ecosystems.
Strong understanding of enterprise data governance, metadata management, lineage, and data quality practices.
Experience creating architecture deliverables, technical roadmaps, and solution diagrams using architecture tooling.
Excellent communication, collaboration, stakeholder management, and documentation skills.
Self-motivated, team-oriented mindset with a humble, low-ego approach to leadership and problem solving.
Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field, or equivalent practical experience.
Authorization to work in the United States.

Nice To Haves

Utility industry experience, including electric, gas, energy, or telecommunications organizations.
Experience with Salesforce data models, architecture, and integration patterns.
Experience with SAP, SAP IS-U, or related utility platforms.
AWS Aurora experience and expertise with relational database architectures.
Knowledge of enterprise data fabrics and data mesh concepts.
Experience supporting Customer 360 implementations.
Exposure to AI-assisted architecture, AI enablement initiatives, or AI agent development.
Experience working with major system integrators or enterprise software implementation partners.
AWS, Databricks, TOGAF, or related cloud and architecture certifications are welcomed but not required.

Responsibilities

Design and architect customer-domain data products that support enterprise business objectives.
Define scalable ingestion and integration patterns across Salesforce, billing platforms, operational systems, and third-party applications.
Architect end-to-end data pipelines into a Databricks Lakehouse environment hosted on AWS.
Establish and maintain system-of-record and system-of-truth standards across customer data ecosystems.
Evaluate and define data access strategies that balance operational performance, governance, scalability, and business needs.
Guide data migration, conversion, and integration architecture for CIS modernization initiatives.
Collaborate with solution architects, data engineers, business stakeholders, and vendors to deliver enterprise architecture solutions.
Ensure alignment with enterprise governance standards, including data quality, lineage, metadata management, and compliance requirements.
Create and maintain architecture artifacts, diagrams, standards, and technical documentation.
Provide architectural leadership and oversight for modernization efforts, data movement strategies, and future-state data platforms.
Support innovation initiatives focused on AI readiness, advanced analytics, and data product ownership.