Director of Data

Stord•Atlanta, GA

About The Position

This is your chance to own the entire data function that powers the next generation of AI-driven logistics. We're looking for a visionary data leader who can unify data engineering, analytics, and data science into a single, coherent function that serves as the supply side of intelligence for all of Stord. You'll scale and advance our data platform while building the data science capabilities that feed real-time AI features across the product. You won't just run a data engineering team - you'll set the data strategy, lead a multidisciplinary team of engineers, analysts, and scientists, and define how Stord turns raw data into competitive advantage. What makes this role transformational: Unified data ownership: Lead data engineering, data analytics, and data science as one coherent function - eliminating the silos that slow AI-driven companies down Platform at scale: Advance and scale our cloud-native data platform to meet the demands of a high-growth, AI-first business processing billions in commerce Data science strategy: Own the modeling agenda - from demand forecasting and EDD prediction to experimentation frameworks and feature engineering ML-ready data infrastructure: Build the pipelines, feature stores, and training infrastructure that the AI Engineering team consumes to ship product Executive influence: Shape technical and organizational data strategy with direct reporting to SVP Product & Engineering How the org works around this role: You own the supply side - clean data, reliable pipelines, accurate models, and the infrastructure that produces them. The AI Engineering team and AI product managers own the demand side - prioritizing and turning those assets into shipped product features. The boundary between data science (model building) and AI engineering (model deployment and serving) is a clean, intentional interface, not a grey area.

Requirements

Track Record of Building Data Functions, Not Just Data Teams
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
The Technical Depth We Need
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
The Leadership Mindset We're Looking For
Integrator, not silo builder: You see data engineering, analytics, and science as one function, not three separate empires
Architect's vision: You design systems and team structures that scale 10x beyond current needs
Business translator: You can explain modeling trade-offs to a CFO and infrastructure trade-offs to a CPO
Team builder: You attract top talent across three different hiring profiles and create environments where each discipline does its best work
AI-forward thinking: You understand the difference between a BI data warehouse and an AI-first data platform, and you've built the latter

Nice To Haves

You've run a unified data function at a high-growth company - engineering and science under one roof
You have hands-on experience with both traditional EDW (Snowflake, BigQuery) and modern ML infrastructure (feature stores, training pipelines)
You've built real-time data systems supporting customer-facing applications with strict SLA requirements
You've defined the interface between a data science team and a model deployment team - and made it work in practice
You've led data integration work through M&A activity - ideally multiple acquisitions at different scales
You have experience in logistics, commerce, supply chain, or high-volume operational data domains

Responsibilities

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
Lead Data Science Strategy
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
Build ML-Ready Data Infrastructure
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
Lead M&A Data Integration
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
Scale a High-Performance Multidisciplinary Team
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
Drive Direct Business Impact
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