Engagement Manager – Data & AI
Experience : 15 -18 years
Location : Bangalore
Role Summary
The Engagement Manager will act as the single point of contact (SPOC) from the Data & AI practice for regional sales and account teams, supporting presales, solutioning, and delivery governance. This role bridges business and technology, ensuring that solutions are well-structured, proposals are compelling, and deliveries adhere to best practices. The individual will bring strong travel domain expertise, a solid understanding of Data & AI, and the ability to translate business needs into scalable solutions.
Responsibilities
Presales & Solution Support
- Sales Enablement: Partner with regional sales and account teams to identify and shape Data & AI opportunities.
- Solutioning: Collaborate with technical teams to define solution approaches, architecture, and scope aligned with client needs.
- Proposal Development: Contribute to RFP responses, proposals, and effort estimations.
- Pitch Support: Create and deliver high-impact presentations and solution walkthroughs to support deal pursuits.
Delivery Governance & Oversight
- Delivery Alignment: Ensure projects are executed in line with defined architectures, best practices, and quality standards.
- Program Oversight: Provide governance across engagements, tracking progress, risks, and dependencies.
- Issue Resolution: Act as an escalation point to resolve delivery challenges in collaboration with delivery teams.
- Best Practices: Drive adoption of standard methodologies, reusable assets, and frameworks across engagements.
Practice Representation
- SPOC for Practice: Act as the primary interface between the Data & AI practice and regional teams.
- Capability Positioning: Help position the practice's capabilities across Data Engineering, BI, and AI/ML in relevant opportunities.
- Internal Alignment: Ensure seamless coordination between sales, account teams, and delivery units.
Domain & Advisory Support
- Travel Domain Expertise: Provide contextual insights and guidance for airline/travel use cases (e.g., revenue management, customer analytics, operations).
- Use Case Definition: Help shape problem statements and define high-value Data & AI use cases.
- Emerging Trends: Bring awareness of trends such as cloud data platforms and Generative AI to enhance solution relevance.
Asset & Capability Development
- Reusable Assets: Contribute to development of pitch decks, case studies, solution frameworks, and accelerators.
- Knowledge Sharing: Support capability building within the practice and across regional teams.
Requirements
Education and Experience
- Bachelor's degree in Engineering, Technology, or related field; MBA preferred.
- 15–20 years of experience in consulting, presales, or delivery roles within Data & AI or related domains.
Domain & Technical Skills
- Strong domain knowledge in airlines, travel, or transportation.
- Conceptual understanding of:
- Data Engineering (data pipelines, data platforms)
- Data Warehousing & BI (reporting, dashboards, analytics)
- AI/ML (predictive analytics, ML lifecycle)
- Hands-on experience in at least one area (Data Engineering, BI, or AI/ML).
- Awareness of cloud ecosystems and emerging trends such as Generative AI.
Core Competencies
- Stakeholder Management: Ability to work effectively with sales, account teams, and delivery units across regions.
- Communication: Strong verbal and written communication skills, with the ability to simplify complex concepts.
- Presentation Skills: Proven ability to create compelling, executive-level presentations and storytelling decks.
- Consultative Thinking: Ability to translate business needs into structured solution approaches.
- Program Oversight: Experience in managing multiple engagements, risks, and dependencies.
Additional Requirements
- Willingness to travel to client locations globally for short-term assignments (2–3 weeks at a time).
Preferred Qualifications
- Experience in IT services or consulting organizations.
- Exposure to large-scale data transformation or AI-led programs in the travel domain.
- Familiarity with estimation models, solutioning frameworks, and commercial constructs.
