Director of Engineering, Bolt

• We are seeking a visionary and experienced Director of Engineering, Data & AI to lead this crucial function
• You are a leader with deep technical expertise in modern data engineering, analytics, and machine learning, coupled with a proven ability to build and scale high-performing teams
• You thrive in ambiguity, love tackling complex technical challenges at scale, and are passionate about leveraging data to create tangible business value
• If you're excited by the prospect of shaping the future of online identity and commerce through data, we want to hear from you!
• BA/BS degree in a technical field (e.g., Computer Science, Engineering, Mathematics, Statistics) or equivalent practical experience
• 7+ years of progressive engineering leadership experience, including at least 2+ years managing managers or senior technical leads
• Significant hands-on engineering background (typically 10+ years total experience) with demonstrated expertise in designing, building, and operating large-scale data systems
• 5+ years of direct, hands-on experience and deep understanding of modern data stacks and concepts, including:
• Data warehousing (e.g., Snowflake, BigQuery, Redshift)
• Data processing and streaming (e.g., Spark, Flink, Kafka, Airflow)
• Data modeling, ETL/ELT development
• ML infrastructure (MLOps, feature stores, model serving, experimentation platforms)
• Business Intelligence and Analytics platforms
• Proven ability to define technical strategy, develop multi-quarter roadmaps, and lead teams to execute successfully against them
• Experience leading engineering teams in a fast-paced, high-growth technology environment (experience at venture-backed startups or established large-scale tech companies is highly relevant)
• Excellent communication, interpersonal, and stakeholder management skills, with the ability to influence and align diverse groups
• Strong problem-solving skills and the ability to navigate ambiguity and make sound technical and strategic decisions

Desirable

• Leadership & Team Management:
• Lead, mentor, and grow a high-performing, cross-functional team of data engineers, machine learning engineers, and potentially analysts/data scientists
• Foster a culture of innovation, collaboration, technical excellence, and accountability within the team
• Manage performance, support career development, and champion diversity and inclusion
• Recruit top-tier engineering talent to scale the team effectively
• Effectively manage and develop technical leads and potentially first-level engineering managers within your organization
• Strategy & Vision:
• Define, articulate, and drive the long-term technical vision and strategic roadmap for Bolt's data platform, ML infrastructure, and AI capabilities
• Partner closely with Product Management, Design, other Engineering leaders, and business stakeholders (e.g., Risk, Growth, Finance) to align data/AI initiatives with company objectives and user needs
• Stay abreast of industry trends and emerging technologies in the data and AI space, incorporating relevant advancements into the team's strategy
• Technical Execution & Architecture:
• Oversee the design, development, deployment, and operation of scalable, reliable, and cost-effective data pipelines, data warehouses/lakes, ML training/serving infrastructure, and analytics systems
• Ensure architectural integrity, sound design principles, and adherence to best practices across all data and ML systems
• Drive technical decision-making, balancing short-term delivery with long-term architectural health and scalability
• Provide day-to-day technical guidance and oversight, ensuring high-quality execution and timely delivery
• Champion robust monitoring, alerting, and operational excellence for all owned systems
• Cross-Functional Collaboration & Impact:
• Build strong relationships and collaborate effectively with stakeholders across the organization to understand their data needs, gather requirements, and deliver impactful solutions
• Clearly communicate technical concepts, strategic plans, progress, and risks to both technical and non-technical audiences, including executive leadership
• Champion data governance, quality, and security best practices throughout the company