Director of Data Engineering

Role Description

Director, Data Engineering & AI leads the strategy, architecture, and execution of enterprise data platforms that power analytics, machine learning, and generative AI capabilities. The role is accountable for building scalable AI-ready data foundations, governing trusted data assets, enabling responsible AI adoption, and delivering business value through modern data products while leading high-performing engineering teams and driving enterprise-wide transformation.

You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

Primary Responsibilities

• AI-Ready Data Platform Strategy
  • Define and drive the enterprise data engineering and AI platform vision
  • Establish scalable data architectures to support analytics, machine learning, GenAI, and agentic AI solutions
  • Ensure data platforms are cloud-native, secure, resilient, and cost-efficient
  • Build AI-ready data foundations including semantic layers, metadata, lineage, and knowledge graphs
• Data Engineering Leadership
  • Lead multiple data engineering teams responsible for ingestion, transformation, storage, and consumption of enterprise data
  • Establish engineering standards, best practices, and reusable frameworks
  • Oversee development of data pipelines, data products, APIs, and real-time streaming solutions
  • Drive modernization from legacy platforms to cloud-based architectures
• AI & Machine Learning Enablement
  • Partner with Data Science and AI teams to operationalize ML and GenAI solutions
  • Build feature stores, vector databases, embedding pipelines, and RAG architectures
  • Enable model training, deployment, monitoring, and lifecycle management
  • Define standards for AI observability, explainability, and responsible AI
• Enterprise Data Governance
  • Establish data quality, stewardship, lineage, cataloging, and master data management processes
  • Ensure compliance with regulatory, privacy, and security requirements
  • Implement governance frameworks for AI training data and AI-generated outputs
  • Drive trusted and certified data asset programs
• Data Product Management
  • Champion a data-as-a-product mindset
  • Define ownership, SLAs, and quality standards for enterprise data products
  • Prioritize investments based on business value and AI-readiness
  • Measure adoption, quality, and business impact of data products
• Innovation & Emerging Technologies
  • Evaluate emerging technologies in GenAI, Agentic AI, Data Fabric, Semantic Layer, Knowledge Graphs, and Intelligent Automation
  • Lead proof-of-concepts and enterprise-scale deployment strategies
  • Drive automation of engineering operations using AI-powered tooling
  • Promote innovation culture across engineering teams
• Business & Stakeholder Engagement
  • Partner with business, product, analytics, and technology leaders to identify AI-powered opportunities
  • Translate business objectives into scalable data and AI capabilities
  • Communicate technology strategy and value realization to executive leadership
  • Influence investment decisions and roadmap priorities
• Financial & Operational Management
  • Own platform budgets, vendor management, and resource planning
  • Optimize cloud costs and platform utilization
  • Establish KPIs for platform reliability, performance, and productivity
  • Ensure operational excellence and adherence to SLAs
• Talent Development
  • Recruit, mentor, and develop high-performing data engineering and AI engineering teams
  • Build organizational capabilities in cloud, analytics, MLOps, GenAI, and data governance
  • Create career growth paths and succession plans
  • Foster a culture of innovation, accountability, and continuous learning
• Key Success Metrics
  • Data quality and reliability
  • AI adoption and business value realization
  • Platform availability and performance
  • Data product usage and customer satisfaction
  • Engineering productivity and automation
  • Cloud cost optimization
  • Regulatory and governance compliance
  • Team engagement and retention

Qualifications

• Undergraduate degree or equivalent experience
• Hands-on experience with AI, creating agentic solutions
• Solid knowledge of Unix and shell scripting
• Solid understanding of DWH principles, Spark/Databricks, Azure Architecture
• Solid understanding of Data Architecture and Azure Cloud
• Understanding of QA and testing automation process
• Understanding and knowledge of Agile

Benefits

• Comprehensive benefits package
• Incentive and recognition programs
• Equity stock purchase
• 401k contribution (all benefits are subject to eligibility requirements)

Application Deadline

This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.