Role Description The Databricks Field Engineering team activates and accelerates the value our customers get from their data and AI. Major Enterprises in the U.S. are leveraging the Databricks Data Intelligence Platform to securely deliver AI’s potential to every audience, at scale. Our Field Engineering team is hiring a dynamic Solutions Architecture leader who is not only focused on team culture but also can translate their team vision into a tangible and effective strategy in order to drive our customers’ Data + AI experiences forward. As a Field Engineering Manager, you will manage a team of Solutions Architects (SAs) for the Manufacturing Segment of Databricks’ Field Engineering organization. You will lead a dynamic team of pre-sales professionals focusing on: - Enterprise software - Big data/analytics - Data engineering - Data science - AI You will partner with Sales (and other Field Engineering technical segments) to increase revenue and help customers become wildly successful. You'll scale and maintain an outstanding Field Engineering team that is efficient in its operations to help accelerate Databricks’ growth in the market. The impact you will have: - Hire, train, grow, and manage a bar-raising team of Solutions Architects for a company in high-growth mode. - Make your customers extremely successful with Databricks and provide outsized value to their businesses. - Maintain a robust hiring pipeline at all times. - Establish relationships across the business to make your customers and team successful. - Partner with sales leadership to hit sales and consumption targets while ensuring customer success. - Keep your team of SAs ahead of the technical curve, ensuring continuous learning and advanced knowledge of the data+AI technology stack. Qualifications - 3+ years of experience building and leading technical pre-sales teams - hiring, onboarding, coaching, and enabling pre-sales professionals. - 7+ years of experience in the Data + AI space with a technical product (i.e., data warehousing, big data, data science, machine learning, or AI). - A deep technical understanding of the impact that Data + AI can drive within various industry segments. - Technical competence to earn the trust of Engineering talent and leadership at Databricks. - Trusted advisor to technical executives who guide strategic data infrastructure decisions. - Experience hiring pre-sales professionals, ramping them up to be successful, promoting them into larger roles, identifying and addressing performance issues, and implementing improvement plans. - Create a positive team culture and help foster working relationships between Field Engineering, Sales, and other important internal partners. - Experience working with other teams such as Product Management, Engineering, and Customer Success. - Demonstrated architectural influence. Able to influence and review complex architectures, guiding your team and customers toward ideal solutions that scale. Requirements - Experience leading a team through best practices for technical qualification, proof of concepts, architecture discussions, and product demonstrations. Benefits - Comprehensive benefits and perks that meet the needs of all employees. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. Zone 2 Pay Range: $172,500 — $237,150 USD

• Build and embed value engineering capability across the portfolio, including ROI modelling, business case development, and economic impact quantification • Ensure customer value is consistently articulated, evidenced, and tied to commercial outcomes • Develop practical frameworks and tools that can be adopted and sustained within PortCos • Work directly within these businesses to embed discipline, not overlay from a distance • Elevate solution consulting standards across the portfolio, including qualification, demo strategy, and deal preparation

Role Description The Manager, Software Development leads a high-performing engineering team responsible for delivering reliable, scalable, and high-quality software to customers. This role combines strong people leadership, technical judgment, delivery accountability, and practical AI leadership. The manager is expected to build an environment where engineers take ownership, collaborate effectively, continuously improve their craft, and use modern AI-enabled development practices to deliver better outcomes faster. This leader is accountable for the health, productivity, and impact of the team. They set clear expectations, guide technical direction, remove impediments, coach engineers, and ensure the team delivers software on time without compromising quality, reliability, security, or maintainability. What you’ll get to do - Lead, coach, and develop a team of software engineers, creating a culture of ownership, accountability, collaboration, and continuous improvement. - Drive delivery of high-quality software with performance, reliability, scalability, security, and maintainability top of mind. - Establish, communicate, and enforce engineering standards, including code review practices, design principles, testing expectations, operational readiness, and documentation. - Provide technical leadership and sound judgment in architectural and design decisions, especially where decisions affect other teams, platforms, customers, or the company at large. - Guide the evolution of the team’s architecture to support future product requirements, business priorities, operational needs, and long-term scalability. - Define and manage service-level expectations for the team’s area of ownership, including SLAs, operational metrics, reliability targets, and incident response practices. - Partner with product, architecture, operations, security, and other engineering teams to align priorities, clarify requirements, manage dependencies, and deliver business value. - Oversee day-to-day execution, including planning, estimation, prioritization, risk management, and delivery tracking. - Ensure the team makes realistic commitments, delivers against those commitments, and continuously improves predictability and execution quality. - Remove technical, organizational, and process impediments that slow the team down or reduce engineering effectiveness. - Promote best practices in software development, including clean design, automated testing, CI/CD, observability, secure coding, and operational excellence. - Lead adoption of AI-assisted engineering practices across the team, helping developers use AI tools effectively, responsibly, and consistently to improve productivity, quality, learning, and delivery outcomes. - Establish clear standards for AI usage in the software development lifecycle, including code generation, code review support, testing, documentation, debugging, refactoring, design exploration, and developer enablement. - Coach engineers on how to evaluate AI-generated output critically, validate correctness, protect sensitive information, avoid over-reliance, and maintain strong engineering judgment. - Identify practical opportunities to use AI to improve team workflows, reduce toil, accelerate delivery, improve software quality, and increase developer effectiveness. - Measure and improve the quality of AI adoption by tracking outcomes such as developer productivity, code quality, review effectiveness, test coverage, cycle time, defect rates, and team confidence with AI-enabled workflows. - Stay current with emerging AI development capabilities and guide the team in adopting tools and practices that create real delivery value, not just experimentation for its own sake. Skills and experience we value - Proven experience leading software engineering teams that deliver production software at scale. - Strong technical background with the ability to guide architecture, design, code quality, operational readiness, and engineering best practices. - Demonstrated ability to lead teams through planning, estimation, delivery, production support, and continuous improvement. - Experience coaching engineers, developing talent, setting expectations, giving feedback, and supporting career growth. - Strong communication and collaboration skills, with the ability to influence technical and non-technical stakeholders. - Proven ability to help developers adopt AI-assisted software development practices in ways that improve delivery, quality, and engineering effectiveness. - Practical understanding of how AI can be applied across the software development lifecycle, including coding, testing, documentation, debugging, design, and knowledge sharing. - Ability to balance innovation with governance, ensuring AI tools are used responsibly, securely, and with appropriate human oversight. - Strong ownership mindset with a track record of delivering results, improving team performance, and creating a culture of accountability. Success in This Role Looks Like - The team delivers reliable, scalable software predictably and with high quality. - Engineers understand and follow clear development, code review, testing, and operational standards. - Architecture evolves in line with product direction and future business needs. - Developers are more effective because AI is embedded into practical engineering workflows, not treated as a side experiment. - AI adoption improves measurable delivery outcomes while maintaining quality, security, and engineering discipline. - The team demonstrates strong ownership, collaboration, technical judgment, and continuous improvement. Benefits - Medical, dental, vision, and life insurance. - 401k plan (plus match). - Global Employee Stock Purchase Plan. - Unlimited Time Away From Work (in lieu of accrued vacation time). - 10 paid US holidays. - Up to 80 hours of paid sick time. - 17 weeks of paid parental leave, subject to the terms of the applicable policy or program. - Opportunities for community impact, including volunteer days and charity initiatives.

• Stakeholder Management: Steward the end-to-end planning, execution, and delivery of the platform-deployed machine learning applications. • Cross-Functional Coordination: Act as the primary point of contact between the customers and ML engineers, translating technical requirements from different gaming-adjacent domains to Machine Learning. • Product Prioritization: Exhibit strong judgement in when to deploy off-the-shelf commercial solutions, customize open-source solutions, or initiate greenfield applied science research projects based on product needs.