The Role
Leads business and technology discovery for Data & AI transformation initiatives. Facilitates stakeholder workshops, assesses processes, systems, data, integrations, and platform maturity, prioritizes use cases, defines solution options, supports rapid prototypes, and develops target-state architecture, MVP recommendations, and implementation roadmaps. Bridges business stakeholders, architects, data and AI specialists, and engineering teams while delivering executive-ready recommendations.
Summary Generated by Built In
Anblicks is seeking an experienced Onsite Business Solutions Architect to lead an intensive Data & AI discovery and assessment engagement. This individual will serve as the bridge between business stakeholders, domain SMEs, data/AI architects, and engineering teams, converting evolving business priorities into well-defined use cases, solution options, prototypes, and an actionable implementation roadmap.
The role will operate in an environment where requirements are still evolving, so the ideal candidate must be comfortable leading structured discovery rather than waiting for fully defined requirements. This aligns directly with the discussion that the engagement is still in the discovery stage and needs someone who can establish clarity before scaling the engineering POD.
Key Responsibilities:
1. Lead Business & Technology Discovery
- Conduct intensive discovery workshops and stakeholder interviews across business, operations, data, and technology teams.
- Understand current business processes, operational pain points, systems, data sources, workflows, and existing technology capabilities.
- Translate business challenges into clearly defined Data, Analytics, AI, and Agentic AI opportunities.
- Develop current-state and target-state views covering people, process, data, applications, integration, and technology.
The engagement discussed multiple areas including workforce lifecycle management, work execution, commercial intelligence, asset reliability, and a common Data & AI foundation.
2. Define and Prioritize Use Cases
- Identify high-value business and operational intelligence use cases.
- Define business outcomes, functional requirements, data requirements, dependencies, KPIs, and acceptance criteria.
- Facilitate prioritization based on business value, feasibility, data readiness, complexity, and time-to-value.
- Convert ambiguous business ideas into executable work packages for the D&A POD.
Priority areas discussed include Commercial Intelligence, Operational Intelligence, Asset Reliability, guided technician workflows, dynamic playbooks, and QA/QC automation.
3. Data & Technology Assessment
- Assess existing data sources, operational systems, integrations, data quality, master-data challenges, and platform maturity.
- Work with architects and engineers to evaluate the appropriate build vs. buy vs. extend approach.
- Help define the common data foundation, semantic/context layer, integration patterns, and AI/agent opportunities.
- Understand modern data platforms such as Snowflake, APIs/integration patterns, analytics, GenAI, and Agentic AI sufficiently to facilitate architecture and design decisions.
The proposed direction includes a common data foundation with operational intelligence layered over existing platforms rather than unnecessarily replacing best-in-class execution systems.
4. Drive Rapid Prototyping
- Partner with Data Engineers, AI/Agent Engineers, architects, and Full Stack Engineers to translate priority use cases into rapid prototypes and PoCs.
- Define prototype objectives and success criteria.
- Facilitate continuous business feedback and iterate prototypes based on stakeholder input.
- Ensure prototypes demonstrate tangible business value, not simply technical capability.
Rapid prototyping was specifically identified as important alongside discovery, rather than treating the engagement as a product-management-only exercise.
5. Develop Roadmap & Recommendations
- Translate discovery findings into concrete deliverables and a prioritized implementation roadmap.
- Define near-term quick wins, MVP opportunities, foundational capabilities, and longer-term initiatives.
- Document solution options, dependencies, risks, assumptions, and recommendations.
- Provide leadership-ready recommendations for investment and implementation decisions.
Expected Deliverables:
The role should own or coordinate production of:
- Current-State Discovery & Assessment
- Business Pain Point and Opportunity Assessment
- Use Case Inventory & Prioritization
- Data Source / System / Integration Assessment
- Data & AI Platform Gap Assessment
- Target-State Conceptual Architecture
- Build / Buy / Extend Recommendations
- Rapid Prototype / PoC Definition and Validation
- Prioritized Data & AI Roadmap
- MVP Scope and Implementation Recommendations
- Executive Discovery Readout and Recommendations
Required Skills & Experience:
- 10+ years of experience across Data & Analytics, digital transformation, enterprise applications, solution consulting, business architecture, or related technology programs.
- 7+ years leading complex discovery, assessment, solution definition, or transformation initiatives.
- Strong combination of business analysis + solution architecture + consulting capabilities.
- Proven ability to conduct executive, business SME, and engineering-level discovery workshops.
- Strong understanding of:
- Enterprise Data & Analytics architecture
- Data platforms and data integration
- Snowflake or comparable cloud data platforms
- APIs and enterprise application integration
- Data modeling, semantic/context layers and master data
- BI and advanced analytics
- GenAI / Agentic AI concepts and use cases
- Rapid prototyping and MVP definition
- Experience assessing heterogeneous environments containing enterprise platforms, SaaS applications, spreadsheets, SharePoint, and manually managed business processes.
- Ability to translate business problems into technical architecture and executable engineering backlog.
- Strong executive communication, presentation, workshop facilitation, and stakeholder-management skills.
Preferred Experience:
- Experience in critical infrastructure, facilities, asset management, field services, construction, or engineering operations.
- Exposure to platforms such as Salesforce, Primavera P6, Procore, Snowflake, SharePoint, or comparable operational/data platforms.
- Experience with commercial intelligence, workforce intelligence, field-service workflows, asset reliability, or QA/QC use cases.
- Experience working with distributed onsite/offshore Data & AI engineering PODs.
Skills Required
- 10+ years of experience across Data & Analytics, digital transformation, enterprise applications, solution consulting, business architecture, or related technology programs
- 7+ years leading complex discovery, assessment, solution definition, or transformation initiatives
- Strong business analysis, solution architecture, and consulting capabilities
- Experience conducting executive, business SME, and engineering-level discovery workshops
- Understanding of enterprise Data & Analytics architecture
- Understanding of data platforms and data integration
- Experience with Snowflake or comparable cloud data platforms
- Understanding of APIs and enterprise application integration
- Understanding of data modeling, semantic/context layers, and master data
- Understanding of BI and advanced analytics
- Understanding of GenAI and Agentic AI concepts and use cases
- Experience with rapid prototyping and MVP definition
- Experience assessing heterogeneous environments with enterprise platforms, SaaS applications, spreadsheets, SharePoint, and manually managed processes
- Ability to translate business problems into technical architecture and executable engineering backlogs
- Strong executive communication, presentation, workshop facilitation, and stakeholder-management skills
- Experience in critical infrastructure, facilities, asset management, field services, construction, or engineering operations
- Exposure to Salesforce, Primavera P6, Procore, Snowflake, SharePoint, or comparable operational/data platforms
- Experience with commercial intelligence, workforce intelligence, field-service workflows, asset reliability, or QA/QC use cases
- Experience working with distributed onsite/offshore Data & AI engineering teams
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The Company
What We Do
Anblicks is a Cloud Data Analytics Company based out of Dallas, TX, with offices in USA, India, and Australia. Since 2004, Anblicks has been helping customers by bringing value to their data and implementing modern data architecture and advanced analytics solutions in the cloud.
