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.