Data Cloud & AI Tech Architect (Tech Architecture/Solution Architecture)

Accenture•Kirkland, WA

1d•$80,400 - $266,300•Hybrid

About The Position

Accenture’s Salesforce practice is seeking a Data Cloud and AI Tech Lead within the Life Sciences Salesforce Business Group. This role operates between architecture and execution, translating senior architect designs into buildable solutions for delivery teams. The Tech Lead will own solution design documents, lead technical workstreams, and ensure architectural integrity throughout the program lifecycle. Fluency in Salesforce Data Cloud, Agentforce, and Life Sciences CRM platforms is essential. The role requires thriving in complex environments with multi-workstream programs, distributed teams, and challenging client data situations, tight timelines, and critical architecture decisions. The individual will bring clarity to ambiguous technical problems and proactively lead junior team members.

Requirements

Minimum of 6 years of Salesforce delivery experience, with meaningful time in both functional and technical roles on full-lifecycle implementations
Minimum of 2 years experience as a Solution Architect or senior technical lead on at least two end-to-end Salesforce programs
Minimum of 2 years working knowledge of data architecture, identity resolution, segmentation, and real-time activation patterns
Minimum of 1 year hands-on experience designing and implementing Agentforce use cases, including AI-powered workflow automation
Integration experience — practical experience designing integrations across CRM, data platforms, and external systems
Minimum of 2 years experience in client-facing consulting environments leading technical workstreams in Agile or hybrid delivery frameworks
Bachelor’s degree in Computer Science, Engineering, or equivalent (minimum 12 years) work experience. If Associate’s Degree, must have minimum 6 years work experience

Nice To Haves

Experience with Salesforce Life Sciences Cloud, Health Cloud, or equivalent pharma/biotech CRM; understanding of commercial and patient-facing use cases
Experience designing or delivering Agentforce Voice, contact center AI, or conversational agent solutions in Life Sciences
Familiarity with Veeva Vault CRM, Veeva CRM, or the Veeva-to-Salesforce migration landscape and integration patterns
Agentforce Specialist, Data Cloud Consultant, Sales/Service Cloud Consultant, Health Cloud Accreditation, or Certified Application Architect
Experience managing technical delivery across onshore/offshore distributed teams
Prior experience contributing to RFP responses or solution design in a pursuit context

Responsibilities

Solution design ownership — lead end-to-end solution design across assigned workstreams, including data model, integration patterns, security model, and platform configuration; keep SDDs, architecture decision records, and data flow diagrams current throughout delivery
Agentforce & AI delivery — own technical design and build for Agentforce use cases — patient services agents, HCP engagement agents, Voice AI integrations — across active Life Sciences programs
Data Cloud workstreams — lead Data Cloud implementation tracks including data ingestion, identity resolution, unified profile design, segmentation, and activation; identify and resolve data quality and governance issues
Integration delivery — design and implement integrations between Data Cloud, CRM, external data sources, and Agentforce capabilities; make architecture tradeoffs across middleware options (MuleSoft, REST/SOAP, Salesforce Connect)
Bridge function — serve as the connective layer between functional requirements and technical delivery; align architects, developers, and client stakeholders on design decisions and scope boundaries
Technical leadership — lead design sessions, code reviews, and deployment governance; manage solution risks, design gaps, and cross-workstream dependencies; escalate proactively
Team development — guide and mentor L8 architects and analysts; set technical standards across the team and build capability in Data Cloud and AI delivery practices
Pre-sale contribution — support estimation, scoping, and staffing conversations during pursuit phases; contribute to architecture sections of RFP responses and orals
Practice contribution — build accelerators, methodology assets, and LS-specific AI use case patterns that scale across the practice