Cross-Practice Delivery Leadership 

• Lead the technical delivery approach on large, multi-disciplinary engagements spanning data engineering, data visualization, data governance, and AI. 

• Orchestrate a unified solution approach across capability leads, integrating each discipline’s technical direction into one coherent delivery plan with clear sequencing, dependencies, and shared standards. 

• Own delivery approach rather than solution design within any single discipline: how the work is structured, sequenced, staffed, and de-risked, and where the technical decision points sit. 

• Identify delivery risk early and intervene on engagements drifting on approach, quality, or coherence across workstreams. 

Practice Maturation 

Priorities are set with the Practice Manager, each carried from assessment through to an adopted standard. 

• Delivery Excellence: baseline delivery standards, reusable accelerators and templates, engineering practices informed by DORA capabilities, and adaptive delivery approaches that balance program predictability with the experimentation required by data and AI projects. 

• Talent and Engagement Model: technical interview standards and calibration, a consistent hiring bar, skills inventory and workforce visibility, and engagement health practices that protect consultant focus. 

• Scoping and Advisory Excellence: structured recommendation frameworks, strategic opportunity framing that translates vague asks into clarified business problems, and clearer engagement pathways between delivery, presales, and the PMO. 

• Data and AI Fluency: capability assessment, targeted learning pathways mapped to assessment outcomes and to the pace of Microsoft platform change, and improved use of internal data for practice decision-making. 

• Build capabilities so they outlast the person driving them, with documented standards, named owners, and adoption evidence. 

AI-Accelerated Delivery 

• Apply AI tooling and agentic patterns to how the practice delivers value, improving the speed, consistency, and quality of consulting work across all engagements. 

• Evaluate where AI meaningfully changes delivery economics and where it introduces review overhead without net gain and build the evidence to distinguish the two. 

• Fold proven patterns into delivery standards and accelerators so that gains are structural to the practice rather than dependent on individual habit. 

Practice Leadership and Capability Growth 

• Coach architects and consultants across disciplines on delivery approach, consulting judgment, and technical decision-making. This is a player-coach role without direct reporting relationships. 

• Influence the priorities of technical leads where cross-practice coherence, engagement risk, or the practice maturation agenda requires it.  

We are looking for candidates who demonstrate strength across these areas. Deep expertise in every technology listed is not expected. Candidates should not self-select out if they do not meet all of them. 

• Bachelor’s degree in a related technical field, or equivalent professional experience. 

• Ten or more years delivering enterprise data or AI solutions, including substantial experience in a consulting or professional services environment. 

• Expert-level depth in one of data engineering, data visualization, or data governance, with working proficiency in at least two of the others sufficient to lead delivery and evaluate technical decisions across them. 

• Demonstrated experience taking a practice capability from assessment through to an adopted standard. Examples include delivery standards, engineering or DevOps practices, accelerator libraries, interview and calibration frameworks, or competency models. Candidates should be prepared to describe what was adopted, and whether it survived after they stopped driving it. 

• Experience leading technical delivery across more than one discipline simultaneously on large or complex engagements. 

• Applied experience using AI to change how a delivery team works, with a clear account of what improved, how it was measured, and what did not work. 

• Hands-on delivery experience within the Microsoft data and AI ecosystem, which may include Microsoft Fabric, Azure data services, Power BI, Microsoft Purview, Copilot Studio, or Microsoft Foundry, aligned to the candidate’s area of depth. 

• Excellent facilitation, presentation, and stakeholder management skills, including with executive audiences. 

• Ability to work effectively through influence rather than reporting authority.