telus-digital - Director and Practice Lead, Data Engineering

• 1. Strategy: Set the practice vision and align Data & AI capabilities to where enterprise demand is heading. • 2. P&L: Own revenue targets, margin, and growth while being accountable for the practice's full commercial performance. • 3. Talent & Culture: Build a practice where data-driven thinking, innovation, and engineering rigour are the baseline. • 4. Client Impact: Act as a trusted advisor to enterprise CxOs navigating data strategy, AI adoption, and governance. • Define the multi-year vision for the Data Engineering Practice, ensuring our technical capabilities are ahead of the curve for enterprise demand for Data & AI transformation. • Own the full P&L, including pipeline, pricing, delivery margin, and revenue growth, while reporting directly to executive leadership with clear commercial accountability. • Build and sustain senior relationships with Google Cloud partner teams and client executives to generate meaningful deal flow and expand strategic accounts. • Lead the practice's position on ethical AI and data democratisation while making principled, evidence-based bets on where advanced analytics and AI are creating real enterprise value. • Drive investment in reusable IP, data accelerators, and delivery assets that improve consistency, reduce time-to-value, and protect margins at scale. • Serve as a strategic advisor to client C-suite executives, helping them define data strategy, navigate AI adoption, and build the organisational capabilities to sustain it. • Set the standard for engineering and analytical excellence across the practice by hiring well, developing talent deliberately, and building teams that clients trust and return to. • 12+ years in data or AI consulting, including at least 3–5 years in a practice leadership or executive role, not just senior delivery or individual contribution. • Proven P&L ownership: Direct experience managing revenue, margin, and growth targets in a consulting or professional services context. • Data and AI ecosystem depth: Strong command of modern data architecture, AI, and ML trends, cloud-native data platforms, and enterprise data governance. • Executive communication: Able to advise, challenge, and build lasting trust with C-suite stakeholders on complex, ambiguous problems, not just present to them. • Business Development track record: Experience generating pipeline and closing complex, multi-stakeholder engagements, not just managing delivery once work is sold. • Talent leadership: Has hired, grown, and retained senior data engineering and consulting talent in a practice or delivery organisation.