Sigmoid Analytics - Delivery Manager - Data Engineering

About the Role : You will lead delivery for large-scale Data Engineering programs, working closely with clients to architect and drive execution of modern data platforms - data pipelines, cloud migrations, lakehouse implementations, and real-time data processing solutions. You will own end-to-end delivery governance across multiple engagements, act as a senior escalation point for clients, and provide technical and delivery leadership to teams building enterprise-grade data engineering solutions. If you enjoy operating at the intersection of technical depth in Data Engineering and delivery leadership, we would like to talk to you. Required Skills Competencies : - 15-18 years of experience in Project Delivery, Client Engagement, or Data Engineering Delivery Management, with a significant portion focused on Data Engineering programs. - Strong hands-on exposure to Data Engineering tools and technologies, including cloud data platforms - Proven experience in Analytics/Consulting/IT Services, managing complex Data Engineering project execution, architecture reviews, and governance. - Exposure to US/Global clients, with experience handling client escalations. - Ability to manage multiple Data Engineering project teams and pods, mentor Delivery/Engineering Managers and technical leads, and drive engineering process improvement and best practices. - Experience partnering with architecture/technical teams to guide platform decisions, technology selection, and scalability/performance considerations. - Strong background in resource planning, margin management, and organizational design within a Data Engineering delivery org. Key Responsibilities : - Lead the end-to-end delivery of large-scale data engineering projects, ensuring milestones are met within defined timelines and budget constraints. - Manage client relationships by providing transparent status updates, addressing concerns proactively, and aligning technical deliverables with evolving business requirements. - Oversee the performance and professional development of data engineering teams, fostering a culture of technical excellence and accountability. - Mitigate project risks by identifying potential bottlenecks in data architecture or resource allocation and implementing effective contingency plans.