Manager, Solution Engineering Informatica Data Foundations - FINS

Role Overview At Informatica, a Salesforce Company, our employees are empowered to push their bold ideas forward, and we are united by a shared passion for using data to do extraordinary things for each other and the world. We seek innovative thinkers who believe in the power of data to drive meaningful change. The Manager of Solution Engineering is responsible for building, coaching, and scaling a team that translates complex enterprise data environments into clear, outcome-driven AI strategies for customers. You will drive a Data First strategy, ensuring the team can clearly articulate why a trusted, governed data foundation across fragmented ecosystems is required to operationalize AI. This role requires a leader who maximizes team capability. Success is defined by your ability to raise the bar on how the team engages, reducing dependency on direct involvement while improving executive alignment, deal orchestration, and value-based selling. While strong leadership fundamentals are expected, success in this role will be measured by how quickly you scale these capabilities across the team. Responsibilities Lead and Develop Technical Talent: Guide and coach a team of Solution Engineers across experience levels, with a focus on improving how they engage, not just what they present. Build a culture of accountability, preparation, and continuous improvement. Over time, success is measured by the team’s growth and independence. Drive Executive Alignment: Coach the team to identify and align to the priorities of key decision makers and influencers. Develop their ability to move beyond feature-level discussions into business outcomes, architectural tradeoffs, and risk. Orchestrate Complex Deals: Develop the team’s ability to plan ahead, prioritize effectively, and coordinate across stakeholders. Reinforce disciplined deal strategy and ensure progress is maintained through the team without consistent manager intervention. Elevate the Architectural Narrative: Coach the team to independently position multi-ecosystem architectures that unify fragmented data estates into a trusted foundation for enterprise AI. Continue to push beyond feature-function positioning into outcome-driven architectural thinking. Enforce Value Discipline and Project Monitoring: Build and reinforce a structured approach to discovery, value validation, and follow-through. Coach the team to proactively identify risks, address obstacles early, and tie technical engagement to measurable business impact. Balance Coaching vs. Direct Involvement: Demonstrate sound judgment in when to step into deals versus coaching from the sidelines, with a clear bias toward developing team capability over individual contribution. Required Qualifications Technical Aptitude and Leadership Growth Potential: Strong technical foundation with the ability and desire to grow into a leadership role focused on coaching and scaling others. Demonstrated ability to influence beyond individual contribution. Solution Engineering Mastery: Strong understanding of enterprise data management and integration, with the ability to coach others on how these capabilities enable AI, automation, and digital transformation. Technical Breadth: Experience with modern data architectures, including cloud data platforms, integration patterns, and the role of data in supporting Generative AI and Large Language Models. Value-Based Selling Capability: Demonstrated ability to coach consultative and value-based selling approaches, connecting technical solutions to measurable business outcomes. Operational Leadership: Experience managing or supporting SE capacity, deal prioritization, and pipeline execution within a technical sales environment. Degree in Computer Science required (B.S., Master’s, or Ph.D.) Preferred Qualifications Leadership Experience: 5+ years in technical pre‑sales, strategy, or management consulting, with clear evidence of leadership through mentoring, informal team leadership, or driving initiatives in complex sales environments. Enterprise Platform Knowledge: Experience with platforms such as Salesforce Data Cloud, Informatica, MuleSoft, or similar, and an understanding of how to position across heterogeneous ecosystems to deliver a unified data foundation. FINS Domain Knowledge: Experience in Financial Services, including familiarity with regulatory constraints, data fragmentation challenges, and enterprise data modernization efforts. #J-18808-Ljbffr