Principal Software Engineer, AI Tooling & Adoption

About the role

Fivetran is building data pipelines to power the modern data stack for thousands of companies. This unique leadership role combines deep technical expertise with strategic business acumen and change management capabilities. You will be the driving force behind transforming our 500+ engineers into "Bionic Engineers" while implementing Virtual Engineer capabilities to achieve 10% productivity gains in FY26 and 20% in FY27.

• Adoption Management & Leadership (40%)
  • Drive Cultural Transformation: Lead the evolution from AI-curious to AI-native engineering practices across all teams
  • Champion Change Management: Convert skeptics into advocates through data-driven demonstrations of value and hands-on enablement
  • Measure & Accelerate Adoption: Establish KPIs, track progress from the current 50% to 100% adoption, and implement interventions to accelerate uptake
  • Create Communities of Practice: Create forums for knowledge sharing, celebrate wins, and foster peer-to-peer learning
• Technical Leadership (35%)
  • Tool Evaluation & Selection: Continuously assess emerging AI tools (Copilot, Claude Code, Codex, etc.) and make data-driven recommendations
  • Architecture & Integration: Design technical frameworks for integrating AI tools into existing development workflows and CI/CD pipelines
  • Virtual Engineer Implementation: Lead proof-of-concepts for coding agents in standardized tasks (Lite Connectors via CoIL, code refactoring, test generation)
  • Security & Compliance: Partner with Security and Legal teams to ensure AI tool usage protects IP and meets compliance requirements
  • Technical Standards: Establish best practices, guidelines, and guardrails for AI-assisted development
  • Performance Optimization: Analyze and optimize the technical performance and cost-efficiency of AI tool deployments
  • Innovation Pipeline: Maintain awareness of cutting-edge AI developments and identify opportunities for competitive advantage
• Business Leadership (25%)
  • Strategic Planning: Develop and execute the three-phase AI adoption roadmap (Explore & Learn → Enable, Train & Adopt → Core Competency)
  • Budget Management: Oversee $1M+ annual tool budget, optimize spend across 500+ team members, and demonstrate ROI
  • Stakeholder Management: Build relationships with the CEO, Engineering Leadership, Finance, L&D, Legal, and Security teams
  • Business Case Development: Quantify productivity gains, cost savings, and strategic value of AI initiatives
  • Vendor Relations: Negotiate enterprise contracts, manage vendor relationships, and influence product roadmaps
  • Cross-functional Collaboration: Work with Program Managers, Engineering Managers, and other non-IC roles to extend AI benefits
  • Remove Barriers: Identify and systematically eliminate technical, cultural, and process obstacles to AI adoption
  • Communicate Impact: Regularly present adoption metrics, success stories, and ROI to engineering leadership and executives

Skills

• 10+ years of software engineering experience with proven expertise across multiple programming languages and paradigms
• Deep hands-on experience with AI-powered development tools (GitHub Copilot, ChatGPT, Claude, etc.)
• Strong understanding of LLMs, prompt engineering, and AI model capabilities/limitations
• Experience with CI/CD, DevOps practices, and modern software development workflows
• Track record of evaluating and implementing new technologies at scale
• Proven ability to drive technical initiatives across large engineering organizations (500+ people)
• Experience leading without authority and influencing senior technical leaders
• Track record of successful change management in engineering contexts
• Demonstrated ability to convert technical innovations into business value
• Experience presenting to C-level executives and board members
• Experience driving tool adoption across diverse technical teams
• Strong communication skills with the ability to create compelling narratives for different audiences
• Track record of building training programs and enablement materials
• Data-driven approach to measuring and improving adoption metrics
• Patience and empathy when working with skeptics and late adopters