Director, Data Collective

What you'll do

• Own skill mix of Data Collective team; lead hiring and performance management for engineers from mid-level to Principal
• Set technical and cultural standards for team including design, code review, on-call, and cross-team partnership
• Mentor and grow engineers through hands-on design collaboration, technical coaching, and career frameworks
• Partner with engineering leadership on org-wide planning, budgeting, and roadmap tradeoffs; represent team to executives
• Drive architectural decisions across pipeline design, data modeling, lakehouse architecture, and data services
• Contribute to design and architecture of data pipelines, lakehouse, and data services including streaming, batch, petabyte-scale storage and query
• Lead design reviews and set technical standards; raise engineering rigor, observability, and operational excellence
• Make tradeoffs on performance, cost, governance, and reliability; validate team's estimates and risk assessments
• Set team direction on AI-native data engineering including LLMs, RAG, agentic workflows, AI-assisted tooling
• Establish standards for evaluating, trusting, and operating AI-powered systems in production including observability, fallback, governance, cost control
• Identify high-leverage AI applications in data stack such as pipeline optimization, anomaly detection, automated data quality, forecasting, LLM-powered data services
• Own operational posture of data platform including SLOs, on-call health, incident response, continuous improvement
• Manage infrastructure cost footprint across AWS and Databricks; drive cost improvements through architecture and commitment management
• Drive Infrastructure as Code adoption, policy-as-code, governance frameworks (RBAC/ABAC, IAM, SCIM), and CI/CD for infrastructure
• Balance investment in new capability, platform health, and tech debt
• Provide domain expertise to enable business growth through data services and data models
• Partner with Product, Data Science, AI/ML, Platform, and Security teams to ship end-to-end and improve data asset usability and safety
• Serve as senior counsel to internal teams, leadership, and external customers of Data-as-a-Service products
• Communicate clearly from architecture documents and design reviews to executive updates on cost, capacity, and risk

Requirements

• 10+ years of software / data engineering experience with hands-on track record in data platforms, distributed systems, or backend infrastructure
• 4+ years leading and growing engineering teams including hiring, leveling, and performance management of senior and principal-level engineers
• Deep, current technical proficiency including reading code, writing design docs, and leading architecture
• Hands-on experience in big data and distributed data processing in AWS ecosystem (Python, Spark, Kafka/Redpanda, Databricks or similar lakehouse platforms)
• Experience operating data systems at scale: real-time streaming, batch pipelines, data lakes, metadata management, lineage, and governance
• Working knowledge of cloud platform engineering practices: IaC (Terraform), CI/CD, observability, IAM, and cost management
• Track record of leading or contributing to AI/agentic engineering efforts in production
• Hands-on experience operating production vector databases at scale with pipelines and infrastructure to refresh hundreds of millions of vectors daily
• Familiarity with adtech data infrastructure and programmatic ecosystem is a strong plus
• Experience with data security and compliance (PII, CCPA, GDPR)
• Ability to communicate architectural concepts and team strategy to engineers, executives, and board
• Comfort driving technical and organizational decisions in ambiguous, fast-moving environments

Tech stack

Benefits