Senior Consultant – Data Strategy & AI Enablement
$105,000–$105,000 year
On-sitePlano, Texas, United States
EXPIREDPlano, Texas, United StatesOn-siteFull Time$105,000–$105,000 yearSenior LevelConsulting ServicesSmall
Full TimeSenior LevelSmallConsulting Services
Job Summary
Lead workstreams across data strategy, analytics, AI readiness, governance, and operating-model design initiatives. Partner with business and technology leaders to assess current-state ecosystems, define future-state capabilities, and create executable roadmaps. Facilitate executive interviews, stakeholder workshops, and prioritization exercises to identify barriers and develop business cases, investment recommendations, and transformation plans. Translate business needs into data requirements, functional specifications, and user stories while guiding junior consultants and supporting vendor evaluations. Contribute to Torq frameworks, assessments, and thought leadership while shaping solution approaches for client presentations.
Required Qualifications
- Ideally 5+ years of experience in data strategy, analytics consulting, management consulting, data governance, business analysis, data product management, digital transformation, or a related client-facing field
- Experience leading workstreams or significant deliverables within complex business and technology initiatives
- Strong problem-solving skills and the ability to structure ambiguous challenges, identify root causes, and develop practical recommendations
- Strong executive communication and storytelling skills, including experience creating and presenting clear recommendations to senior stakeholders
- Experience planning and facilitating interviews, workshops, working sessions, and cross-functional decision-making processes
- Ability to translate between business objectives, functional needs, data requirements, and technical considerations
- Strong stakeholder-management skills and the ability to build credibility across business, technology, data, analytics, risk, and compliance functions
- Ability to create high-quality assessments, roadmaps, operating models, business cases, requirements, and executive presentations
- Experience leading junior team members, reviewing deliverables, and creating structure for collaborative work
- A four-year degree in Business, Information Systems, Data Science, Computer Science, Engineering, Economics, Finance, or a related field, or equivalent professional experience
- Understanding of modern data ecosystems, including source systems, integration, storage, modeling, governance, analytics, and consumption
- Familiarity with cloud data platforms and technologies such as Microsoft Fabric, Databricks, Snowflake, Microsoft Azure, AWS, or Google Cloud Platform
- Experience assessing organizational data maturity and defining pragmatic current-state, future-state, and roadmap recommendations
- Understanding of centralized, decentralized, federated, data mesh, data-product, and analytics operating-model concepts
- Experience with data governance, data quality, metadata, lineage, stewardship, ownership, privacy, access, and compliance
- Ability to define and prioritize reporting, analytics, automation, machine-learning, and generative-AI use cases
- Understanding of the data requirements and organizational capabilities needed to support machine learning and generative AI
- Familiarity with AI-readiness considerations such as data accessibility, quality, governance, security, model risk, responsible AI, adoption, and value measurement
- Experience developing business requirements, functional requirements, user stories, process flows, and acceptance criteria
- Comfort using data to support analysis, build business cases, assess opportunities, and communicate recommendations
- Working knowledge of SQL, business intelligence, data modeling, or analytical tools
Desired Qualifications
- Enterprise data and analytics strategy
- AI strategy and AI-readiness assessments
- Generative-AI use-case identification and prioritization
- Responsible AI and AI governance
- Data-governance implementation
- Data ownership and stewardship programs
- Data-product strategy and product management
- Data mesh and federated operating models
- Analytics and business-intelligence operating models
- Data literacy, adoption, and organizational change
- Data-platform selection and implementation planning
- Master data management
- Metadata management, catalogs, and business glossaries
- Microsoft Purview, Collibra, Informatica, or Unity Catalog
- Power BI, Tableau, Looker, or similar analytics tools
- Process improvement, value-stream mapping, and operating-model transformation
- Financial modeling, benefits realization, and value tracking
- Experience in automotive, airline, insurance, financial services, energy, healthcare, or consumer services
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