Data Engineering Leader

Position Summary

Role Overview: As a Data Engineering Leader, you will serve as both the visionary architect and driving force behind our data strategy shaping the roadmap and leading its successful implementation. Your influence will extend across every facet of our organization, embedding robust Data Governance into the core of how we build and what we deliver. In this hands-on leadership role, you will set the standard for excellence, actively participating in high-impact projects and guiding engineering teams by example. You will mentor and inspire others, fostering a culture of innovation and continuous improvement. Success in this position requires a deep expertise in Data Catalog, Data Marketplace, and DataOps technologies, combined with strong leadership skills and a genuine passion for advancing data-driven solutions. The ideal candidate will be a collaborative partner and trusted mentor, working closely with cross-functional teams to design, develop, and deploy sophisticated data software solutions that elevate our business.

• Strategic Vision and Alignment: Craft and articulate a vision for Data Catalog/Data Marketplace/DataOps as it specifically applies to the product engineering teams in alignment with the US Deloitte Technology Data strategy. Collaborate with diverse stakeholders, including product, engineering, experience, delivery, security, and infrastructure teams.
• Advocacy and Technology Roadmap: Advocate for, develop, and communicate the Data Catalog/Data Marketplace/DataOps implementation approach to the product engineering teams. Ensure the organization is well-informed about objectives, KPIs, technology roadmaps, and progress.
• Craft Mastery and Objectives Realization: Define, measure, and drive the achievement of KPIs. Establish and evolve Data Catalog/Data Marketplace/DataOps domain standards and best practices. Actively be hands-on with design, architecture, and code most of the time, contributing to team velocity, and be actively engaged with engineers across SSDLC. Review code, drive tech debt reduction, and experiment with new tech.
• Capability Evolution and Development: Mentor and develop engineers. Coach and develop skills in modern engineering practices, related to Data Catalog/Data Marketplace/DataOps. Showcase learning and mastery by showcasing experiments internally, speaking at conferences, writing whitepapers or blogs, and leading R&D collaborations.
• Iterative Value Delivery: Embrace an iterative/incremental approach to product engineering. Apply a learning-forward approach to navigate complexity and uncertainty. Ensure alignment with customer and business goals through iterative steps and empirical evidence.
• Customer-Centric Problem Solving: Focus on addressing critical issues faced by customers and users. Align technical solutions with business objectives. Minimize unnecessary technical complexities and overengineering. Drive teams toward peak performance through continuous learning and improvement.
• Expert Proficiency and Continuous Improvement: Possess deep expertise in modern software engineering practices. Identify inefficiencies and opportunities for innovation. Enhance the product engineering operating model to be lean and adaptable. Guide and transform the organization to embrace lean principles and foster a culture of innovation.
• Tech/Quality Risk Management: Ensure appropriate technology use and adoption by engineers. Develop and implement explainable, scalable, reliable, and secure products. Inspire experimentation and quality of code. Identify potential technical risks and develop mitigation strategies.
• Influential Communication: Influence, persuade, and drive decision-making processes. Communicate effectively in both written and verbal forms. Craft clear, structured arguments and technical trade-offs supported by evidence.
• Organizational Engagement and Collaboration: Engage stakeholders at all levels of the organization. Build collaborative and constructive relationships. Co-create and drive momentum and value across multiple organizational levels.

• Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.

• A bachelor's degree in computer science, software engineering, or a related discipline. Experience is the most relevant factor.
• Minimum 10 years of experience in data management, data governance, data structures, database systems, and data modeling.
• Minimum 5 years of experience in managing big data of various forms with data platforms such as Informatica, Databricks and/or Delphix to generate insights and create intelligence.
• Minimum 3 years of experience with cloud hyperscalers like AWS, Azure, or GCP to build cloud-native applications.
• Minimum 2 years of experience leading and managing high-performing data engineering teams.
• Minimum 1 year of experience with AI/ML and GenAI.
• Prior experience implementing advanced data concepts such as data observability, data fabric architectures, and data anonymization techniques.
• Prior experience in modern software engineering practices, including MLOps and deployment techniques such as Blue-Green and Canary, to support A/B testing strategies.
• Prior software engineering experience with an understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations.
• Prior experience using methodologies and tools such as XP, Lean, SAFe, DevSecOps, SRE, ADO, GitHub, SonarQube, etc. to deliver high-quality products rapidly.

• Ability to travel 10%, on average, based on the work you do and products you build.
• Limited immigration sponsorship may be available.