Director of Data

Role Overview

• Own the Full Data Function
• Scale and advance our cloud-native data platform, driving architectural improvements that keep pace with rapid business growth
• Architect data systems that serve both BI and ML workloads at increasing scale without sacrificing reliability or governance
• Establish data governance, quality, and lineage frameworks that support compliance and rapid feature development
• Serve as the single executive accountable for data ROI
• what gets built, in what priority, measured by business impact
• Coordinate distributed analyst governance across business units via dotted-line relationships with embedded analysts
• Own the data science agenda
• define where predictive modeling and statistical analysis create the most leverage
• Drive delivery of core ML use cases: demand forecasting, EDD prediction, routing optimization, and warehouse intelligence
• Establish experimentation frameworks that let the business run rigorous A/B tests and learn faster
• Build feature engineering practices in collaboration with data engineering, ensuring data scientists have clean, model-ready inputs
• Partner with the Decision Science PM to translate business problems into modeling priorities and communicate model outputs to stakeholders
• Design and implement real-time streaming architectures that generate ML features at the freshness AI Engineering requires
• Build and maintain feature stores that give data scientists and AI engineers consistent, versioned access to production features
• Create training data pipelines that allow models to retrain reliably with high-quality, well-governed data
• Define the interface between this team (feature generation, training data quality) and AI Engineering (model serving and deployment)
• making handoffs clean and scalable
• Ensure data infrastructure supports embedded analytics serving hundreds of customers with real-time operational insights
• Assess data quality, infrastructure maturity, and integration complexity
• Lead post-acquisition data integration, migrating acquired datasets into Stord's platform without disrupting existing operations
• Establish repeatable playbooks for absorbing new data sources, schemas, and business logic from acquired companies
• Partner with engineering and business stakeholders to prioritize which acquired data unlocks the most value, and sequence integration accordingly
• Lead data engineering, data analytics, and data science as one team with a shared platform and a unified strategy
• Build hiring and development frameworks that scale from today's 12-18 to a larger org as Stord grows
• Create career paths across three distinct disciplines
• engineers, analysts, and scientists
• while maintaining a cohesive team culture
• Develop the next generation of data leaders within the org; this role is designed to be a stepping stone for future directors of data engineering and data science
• Partner with AI product managers across three tracks (AI Product, Decision Science, Internal Enablement) to align data priorities with product delivery
• Enable demand planning, EDD prediction, and warehouse optimization features by ensuring the underlying data and models are production-ready
• Build data products that contribute directly to revenue growth, operational efficiency, and customer retention
• Establish SLAs and monitoring ensuring 99.9%+ uptime for business-critical data and model serving systems

Requirements

• 10+ years of data leadership with proven ability to run multidisciplinary teams spanning engineering, analytics, and data science
• Full-stack data expertise: You've led both a data engineering function and a data science function
• ideally at the same time
• Cloud-native architecture experience: Deep expertise with GCP, BigQuery, and modern data stack technologies
• Team scaling success: You've grown high-performing data organizations through hypergrowth phases without losing quality or culture
• Executive partnership: Track record of translating technical and scientific work into business impact at the C-level
• Modern data stack mastery: Expert-level experience with BigQuery, dbt, streaming platforms, and BI tools
• Data science fluency: You don't need to write the models, but you need to lead the people who do
• strong understanding of ML concepts, experimentation, and model lifecycle management
• Feature store and ML infrastructure knowledge: Hands-on understanding of how to build and operate the data infrastructure that data scientists and ML engineers depend on
• Real-time systems: Deep understanding of streaming architectures, CDC, and low-latency data processing
• Data governance at scale: Experience implementing data quality, lineage, and compliance frameworks across multiple teams
• M&A data experience: Familiarity with data due diligence, integration planning, and absorbing new data sources from acquisitions.

Tech Stack

• BigQuery
• Cloud
• Google Cloud Platform

Benefits

• Health insurance
• 401(k) matching
• Flexible work hours
• Paid time off
• Professional development opportunities