The Practice Director, Data & AI leads the Data & AI practice within the Presales & Solution Architecture function and reports to the Chief Technology Officer, SVP. This role owns the full strategic, commercial, operational, and talent agenda for the practice across three offering areas: Data Modernization & Governance, AI Transformation & Solutions, and Data Infrastructure & Operations. It blends hands-on technical leadership with business acumen, driving pipeline growth, customer outcomes, and delivery readiness for mid-market clients, with a partner-led approach anchored by Microsoft and complemented by other leading data, AI, and infrastructure platforms. The Practice Director is responsible for setting practice strategy, developing standardized offerings, building the team, and cultivating partner relationships. Success requires close collaboration with sales, delivery, and operations to achieve both short-term deal success and long-term practice maturity.
This role leads the Data & AI practice as a single, integrated practice built around one buyer story: modernize the data estate, put AI to work, and run AI securely and economically. Responsibilities include practice strategy, offering development, field enablement, strategic partner alignment, and growth of professional, managed, and recurring services across data modernization, applied AI, and AI operations. The role is accountable for scaling the practice, translating customer needs, partner platform capabilities, AI adoption trends, security and governance requirements, and delivery capabilities into repeatable solutions, scalable sales plays, and measurable business outcomes.
Key Responsibilities
Practice Strategy and Development
- Define and execute the Data & AI practice strategy in alignment with company objectives, mid-market customer needs, partner and technology roadmap priorities, and market demand, organized around three offering areas: Data Modernization & Governance, AI Transformation & Solutions, and Data Infrastructure & Operations.
- Develop and maintain standardized offerings, reusable assets, and solution blueprints that bring consistency and repeatability, through productized pitch materials, one-pagers, labor models, qualification patterns, and partner-aligned scalable motions that improve sales velocity and focus technical capacity on higher-value opportunities.
- Own the offer and pursuit narrative for Data Modernization & Governance, including data strategy and maturity assessments (along with Strategic Advisory), data architecture and modernization, data integration and engineering, data governance, quality, literacy, and lineage, cloud data platforms, analytics and business intelligence, and data estate discovery and rationalization.
- Own the offer and pursuit narrative for AI Transformation & Solutions (along with Strategic Advisory), including AI readiness assessments, AI strategy and roadmaps, use-case identification and prioritization, AI assistant (including Microsoft Copilot) readiness and adoption, AI agents and agentic workflows, RAG and enterprise knowledge solutions, intelligent automation, custom generative AI applications, and AI adoption and change enablement.
- Own the offer and pursuit narrative for Data Infrastructure & Operations, including AI infrastructure architecture, GPU and accelerated compute, AI-ready virtualization, hybrid and private AI environments, AI security and advanced red teaming, responsible AI and governance, data protection for AI, AI FinOps (token and compute economics), AI observability, model and agent lifecycle management, and managed AI environments. Infrastructure and security components of these offerings are delivered by the Datacenter Modernization and Cybersecurity practices.
- Align practice offerings to existing Blue Mantis capabilities, including Mantis Insights, the AI Readiness Journey, GPU-as-a-Service, Virtualization, Infrastructure modernization, AI security and governance, Advanced AI Red Teaming, and AI FinOps.
- Define working boundaries with adjacent practices: the Data & AI practice owns the offer and pursuit narrative, and partners with Microsoft & Modern Workspace (Copilot readiness, Microsoft Purview), Cybersecurity (AI security and red teaming), and Datacenter Modernization (GPU and AI-ready virtualization) for underlying solution and delivery expertise, and collaborates with Strategic Advisory Services to support executive strategy and roadmapping.
- Establish reusable reference architectures, solution patterns, governance models, and delivery standards for data platforms, RAG, agentic workflows, and AI operations that improve consistency, quality, and scalability across customer engagements.
- Drive go-to-market strategy across the ecosystem, aligning cross-functional teams to accelerate growth and to attach Data & AI offerings to broader cloud, security, and managed services engagements.
- Collaborate with delivery leadership to continuously refine methodologies, standards, and governance models to support delivery excellence.
- Define and evolve practice accelerators and enablement assets that improve presales effectiveness, delivery readiness, and customer outcomes, and embed automation-first principles into data modernization and AI offerings to improve speed, consistency, and operational outcomes.
Client Engagement and Deal Support
- Provide deal leadership and escalation support for complex or high-value Data & AI opportunities.
- Lead direct customer-facing architecture, discovery, proposal strategy, executive pursuit support, solution narrative development, seller enablement content, qualification guidance, objection handling, talk tracks, and field activation materials for complex or strategic Data & AI opportunities.
- Shape complex opportunities that expand practice revenue, increase managed or recurring service adoption, and strengthen customer relationships.
- Collaborate with sales teams to review solution architectures, validate alignment, and close strategic pursuits.
- Advise customers on data and AI strategy, including data readiness, governance, AI use-case prioritization, Copilot and agent adoption, and secure, cost-controlled AI operations, using phased roadmaps.
- Support development of multi-year transformation programs, including business cases, value modeling, investment roadmaps, and phased execution plans.
- Guide executive stakeholders through the business impacts of data and AI initiatives, including data ownership, governance, operating model changes, responsible AI, risk reduction, and cost control.
- Position data readiness, governance, and measurable business outcomes as key value drivers in customer business cases and sales pursuits, connecting AI investment to operational improvement rather than simply reselling AI technology.
- Evangelize the differentiated value of the practice internally and externally, representing the firm at industry events and with clients.
- Support pursuit automation to improve efficiency in presales processes.
Talent and Capacity Management
- Build, lead, and develop the Data & AI solution architect team, including defining role expectations, team structure, and hiring plans aligned to practice growth.
- Manage practice capacity, workload, and resourcing to align with growth goals.
- Recruit, mentor, and develop team members through structured career paths, training programs, and certification roadmaps.
- Build a high-performing culture that emphasizes collaboration, solution quality, client success and goal attainment.
- Align presales staffing models to practice demand, delivery readiness, skills coverage, and utilization targets.
- Maintain balanced presales coverage across data architecture and engineering, analytics and BI, AI and generative AI solutions, and AI infrastructure, security, and governance capabilities.
- Partner with delivery leadership to forecast delivery demand from the Data & AI pipeline and identify delivery resource strategies, including internal staffing, skills gaps, and subcontractor or partner capacity, so sold work can be delivered with quality and margin.
- Develop enablement paths that help team members build deeper practice expertise, broaden adjacent skills, and support evolving customer needs, including Microsoft and other leading vendor data and AI certifications.
- Ensure presales talent can articulate both technical and business impacts of data and AI solutions, including customer outcomes, operational considerations, and adoption requirements.
Partner and Vendor Engagement
- Lead strategic partner alignment for the Data & AI practice, anchored by Microsoft and extending to other leading data, AI, and infrastructure vendors, including joint account planning, co-selling opportunities, funding alignment, solution plays, and partner business reviews across platforms such as Microsoft Fabric, Azure AI Foundry, and Power BI, alongside other leading data and AI platforms.
- Provide direction on Microsoft and other partner specializations, partner designations, delivery partners, tooling partners, and ecosystem capabilities that extend practice capacity, align to market demand, support funding consumption, improve delivery scalability, and expand margin.
- Maximize relevant vendor, alliance, and distributor partner programs to accelerate pipeline development, capture available funding or incentives, and increase customer adoption of practice-aligned solutions.
- Recommend and nurture strategic relationships with complementary data platform, AI infrastructure and compute, and AI security and governance partners to enhance market relevance and solution value.
- Coordinate with delivery and sales leadership to align partner investments, incentives, enablement programs, and field motions to priority Data & AI offerings, orchestrating sales, delivery, PMO, Partner Alliances, CSP, Managed Services, Security, Marketing, and Finance to connect professional services, managed services, CSP, partner capacity, funding, and consumption growth into scalable customer and field motions.
- Incorporate partner platforms, tools, and enablement programs into joint go-to-market strategies and practice offerings.
- Drive partner-supported accelerators, campaigns, funding programs, and solution plays tied to priority practice initiatives.
Performance Management and Reporting
- Track and report on financial and operational KPIs, including pipeline health, win rates, utilization, revenue, and margin.
- Monitor practice revenue growth, pipeline contribution, managed or recurring service attach rates, and customer adoption trends to ensure scalable business performance.
- Apply insights from reporting to refine strategy and drive continuous improvement.
- Advise leadership through Practice Business Reviews, strategic account reviews, and operating cadences, using business context, performance insights, risks, priorities, capacity constraints, margin opportunities, partner alignment, and field feedback to recommend action.
- Adhere to governance cadences (bi-weekly, monthly, quarterly) for accountability across practice outcomes.
- Maintain pipeline hygiene, forecast quality, CRM discipline, opportunity data integrity, and operational reporting required to support leadership visibility, prioritization, and practice performance management.
Skills, Knowledge and Expertise Technical and Strategic Skills
- Deep expertise in data and AI solution architectures, with broad knowledge of adjacent cloud, infrastructure, and security domains.
- Experience developing go-to-market strategies, standardized offerings, and delivery frameworks for data modernization, analytics, and AI solutions.
- Proven ability to manage financial and operational performance metrics.
- Architectural experience with modern data and AI platforms, including Microsoft Fabric, Azure data services, Power BI, and Azure AI Foundry, as well as other leading cloud data and AI platforms, preferred
- Strong understanding of data architecture, integration, governance, and quality patterns, and of AI solution design including RAG, agentic workflows, and generative AI applications.
- Working knowledge of AI infrastructure, security, and operations topics (GPU and accelerated compute, AI-ready virtualization, AI security, responsible AI, AI FinOps, AI observability) sufficient to shape offerings and pursuits delivered in partnership with adjacent practices.
- Strong understanding of current-state mid-market environments, common customer challenges, adjacent technology domains, and operating considerations relevant to the practice.
- Hands-on experience with the methods, tools, frameworks, and operational practices commonly used to deliver data and AI solutions.
Business and Leadership Skills
- Strong leadership and interpersonal skills, capable of influencing executives and cross-functional stakeholders across sales, delivery, and adjacent practices.
- Demonstrated success building and scaling technical teams in presales or practice management functions.
- Skilled communicator, adept at presenting complex data and AI concepts to both technical and business audiences.
Education and Certifications
- Bachelor’s degree in Computer Science, Business, or related field, or equivalent experience.
- Industry-recognized data and AI certifications preferred, including Microsoft credentials such as DP-600 (Fabric Analytics Engineer), DP-700 (Fabric Data Engineer), or AI-102 (Azure AI Engineer), or equivalent credentials from other leading cloud and data platform vendors.
Experience
- 10+ years in IT architecture, presales, or consulting, including 3+ years leading a practice, presales team, or advisory function in data, analytics, or AI.
- Proven track record of building and managing practices or technical teams.
- Experience with leading OEMs, ISVs, and the professional services industry.
- Direct experience leading professional services, presales, delivery, or advisory functions in data, analytics, or AI practice areas.
- Demonstrated success delivering complex customer programs, solution implementations, or transformation initiatives relevant to data modernization or applied AI.
- Experience helping mid-market customers modernize data estates, apply AI to business outcomes, and operate AI securely and economically using Microsoft and other leading platforms.
- Demonstrated success embedding repeatable methods, automation, governance, enablement, or operational discipline into customer programs and internal practice execution.
Key Performance Indicators (KPIs)
- Data & AI pipeline, bookings, and win rates, including managed and recurring service attach.
- Offering traction across the three offering areas, including progression from assessments and readiness engagements to implementation and managed services.
- Partner performance (Microsoft and other strategic partners): partner-sourced pipeline, co-sell wins, funding consumption, and partner designation and specialization progress.
- Shared with delivery leadership: delivery capacity coverage against Data & AI bookings, delivery quality, and client satisfaction.
- Team build-out, enablement, certification progress, productivity, and retention of practice talent.
Base Salary Range: $200,000.to $230,000 Please note: This range is based on our market pay structures. However, individual salaries are determined by a variety of factors including, but not limited to business considerations, local market conditions, and internal equity, as well as candidate qualifications, such as skills, education, and experience.
We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
Reasonable accommodations will be made to enable employees with disabilities to perform the essential functions of the job. This job description is intended to describe the general nature of the work being performed by employees in this role. It is not intended to be a complete list of all responsibilities, duties, and skills required.
