Sr Data Architect – Customer & Enterprise Data

Expleo•Denver, CO

3d•Hybrid

About The Position

Trissential is hiring a Sr Data Architect to join their client’s team. This role is critical for Customer Information Systems (CIS) modernization, Customer 360 initiatives, and enterprise-wide data and AI programs. The architect will be responsible for shaping how customer data flows across the enterprise and enabling analytics, AI, and real-time insights at scale. The role sits at the intersection of architecture, governance, and innovation.

Requirements

7+ years of experience in data architecture or data warehouse design
Strong expertise in Databricks Lakehouse architecture
Hands-on experience with AWS data services (Aurora, S3, integrations)
Experience integrating SaaS platforms, especially Salesforce CRM
Deep understanding of data modeling, data movement, and access patterns
Proven ability to define and enforce data governance, lineage, and quality standards
Experience working across hybrid cloud and on-premise environments
Strong communication, collaboration, documentation, and architectural leadership skills
Proven ability to work effectively within dynamic team structures and motivated to collectively deliver high value to the organization
Bachelor’s degree in Computer Science, Information Systems, or a related field (or equivalent experience)

Nice To Haves

Experience supporting Customer Information Systems (CIS) modernization
Exposure to enterprise data fabric or mesh architectures
Familiarity with AI/ML data enablement use cases
Experience building integration with Salesforce and/or SAP
Utility or regulated-industry experience
Cloud or Databricks or AI certifications

Responsibilities

Design and own customer domain data products aligned to a Customer 360 strategy
Architect end-to-end data ingestion patterns across Salesforce, billing systems, and operational platforms
Define and design data pipelines into the Databricks Lakehouse running on AWS
Establish and enforce system of record and system of truth standards to reduce redundancy and improve trust
Assess and evolve data models within operational databases to support new customer domains
Balance normalized data storage with performance-optimized access patterns for real-time and analytics use cases
Ensure data quality, lineage, and governance compliance across all data flows
Collaborate with database engineers to transition architecture designs into implementation