Director, Data Collective

Sovrn•Boulder, CO

10d•Hybrid

About The Position

Sovrn is a Software and Data business that helps Open Web businesses be and remain independent. We help them understand their business better, operate more efficiently, and make & keep more money. We believe in the freedom and free-flow of information. We believe the Open Web is the largest source of this information. We believe in helping Open Web businesses be and remain Independent. Through Software products and Data solutions we help our customers: Understand their business better , so they can make better decisions Operate their business more efficiently, so they can invest in what matters most Make (and Keep) more money , so they control their own destiny. We're looking for a Director, Data Collective to lead the team that owns Sovrn's data platform end-to-end: the pipelines, lakehouse, data services, and cloud infrastructure that power our exchange, our products, and our customers' decisions. This is a hands-on engineering leadership role. You'll own team composition and hiring; lead architecture and design across the platform; and remain close enough to the code, the systems, and the tradeoffs to make real technical decisions, not just approve them. You'll be working with a strong senior team, a modern stack, and an organization that already uses LLMs and agentic tooling across the data stack. We're looking for a leader who can take what's working from "in use" to "intentional practice." Someone with strong opinions about what high-leverage AI-native data engineering looks like at exchange scale, and the credibility to bring the rest of the org along.

Requirements

10+ years of software / data engineering experience, with a strong 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. You still read code, write design docs, and lead architecture, not just review work
Hands-on experience in big data and distributed data processing in the 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 substantially contributing to AI / agentic engineering efforts in production, not just experimentation, but shipped, operated, and iterated on
Hands-on experience operating production vector databases at scale, including the pipelines and infrastructure to refresh hundreds of millions of vectors on a daily cadence
Experience with data security and compliance (PII, CCPA, GDPR)
Ability to clearly communicate architectural concepts and team strategy at multiple levels, from engineers to executives to the board
Comfort driving technical and organizational decisions in ambiguous, fast-moving environments

Nice To Haves

Familiarity with adtech data infrastructure (SSP, DSP, exchange, or ad server environments) and the programmatic ecosystem (OpenRTB, bid request/response flows, auction mechanics, supply path optimization) is a strong plus

Responsibilities

Own the skill mix of the Data Collective team; lead hiring and performance management for engineers ranging from mid-level to Principal
Set the technical and cultural standards for the team: what "great" looks like in design, code review, on-call, and cross-team partnership
Mentor and grow engineers across levels through hands-on design collaboration, technical coaching, and clear career frameworks
Partner with the broader engineering leadership team on org-wide planning, budgeting, and roadmap tradeoffs; represent the team's work and constraints to executives
Drive architectural decisions across pipeline design, data modeling, lakehouse architecture, and data services layers
Heavily contribute to the design and architecture of Sovrn's data pipelines, lakehouse, and data services: high-throughput streaming, always-on batch, petabyte-scale storage and query
Lead design reviews and set technical standards across the team; raise the bar on engineering rigor, observability, and operational excellence
Stay close enough to the systems to make real tradeoffs on performance, cost, governance, and reliability, and to know when the team's estimates and risk assessments are right
Set the team's direction on AI-native data engineering: where LLMs, RAG, agentic workflows, and AI-assisted tooling create real leverage in a high-throughput adtech environment, and where they don't
Establish standards for how the team evaluates, trusts, and operates AI-powered systems in production: observability, fallback behavior, model governance, and cost control
Identify high-leverage AI applications in the data stack: intelligent pipeline optimization, anomaly detection, automated data quality, forecasting, and LLM-powered data services
Own the operational posture of the data platform: SLOs, on-call health, incident response, and continuous improvement
Own the infrastructure cost footprint of the Data Collective across AWS and Databricks; drive structural cost improvements through architecture, and disciplined commitment management
Drive Infrastructure as Code (IaC) adoption, policy-as-code, governance frameworks (RBAC/ABAC, IAM, SCIM), and CI/CD for infrastructure across the team
Make sure the team is investing in the right balance of new capability, platform health, and tech debt
Provide domain expertise across the organization 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 to make Sovrn's data assets easier and safer to use
Serve as a senior point of counsel to all consumers and stakeholders of Sovrn's data: internal teams, leadership, and external customers of our Data-as-a-Service products
Communicate clearly at multiple levels: from architecture documents and design reviews to executive updates on cost, capacity, and risk