Remote Worker

Job Type

• Architecting the Future: Define and execute a data infrastructure roadmap centered on a Lakehouse architecture that integrates structured and unstructured data, enabling both real-time operational analytics and high-scale AI/ML workloads.
• AI-Ready Foundation: Establish the data governance, cataloging, and lineage frameworks necessary to power secure, trusted AI models and Large Language Models (LLMs) across the enterprise.
• Manufacturing Integration: Partner with OT and Engineering teams to ingest and operationalize IIoT and supply chain data, creating a unified data ecosystem that drives predictive maintenance and factory floor efficiency.
• Modern Data Stack Leadership: Oversee the transition from legacy BI tools to modern, self-service analytics platforms, ensuring the organization has the agility to derive insights from the data lakehouse.
• Data Ops & Governance: Lead the transition to MLOps and DataOps methodologies, ensuring data quality, security, and compliance in an increasingly automated environment.
• Strategic Partnership: Collaborate with business unit leaders to identify and prioritize data products that drive measurable top-line growth or operational cost reductions.
• Team Leadership: Build and mentor a high-performing team of data engineers, ML engineers, and data architects who are comfortable in both cloud-native environments and complex legacy manufacturing systems.

• Strategic Experience: 15+ years in data strategy, architecture, and engineering, with at least 5 years in a leadership role driving organizational change.
• 5+ years in a leadership role managing data & analytics teams.
• Architecture Expertise: Demonstrated experience designing and deploying Lakehouse architectures (e.g., Databricks, Snowflake, or similar) at scale.
• AI/ML Fluency: Proven experience operationalizing AI/ML models within an enterprise environment; deep understanding of data preparation for LLMs and generative AI.
• Cloud Proficiency: Extensive experience with Azure (or equivalent cloud hyperscaler) data stacks (e.g., Synapse/Fabric, ADLS Gen2, Azure AI).
• Tooling: Advanced proficiency in Python, Spark, and SQL; strong experience with CI/CD for data pipelines and infrastructure-as-code.
• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.
• Soft Skills: A "product manager" mindset for data; the ability to translate complex technical architectural debt into business-friendly value proposition

• Experience with data governance frameworks and tools.
• Exposure to advanced analytics, data science, or machine learning initiatives.
• Experience in manufacturing, industrial, or eCommerce environments preferred.

• Ability to work in both office and manufacturing environments.
• Availability to work outside of core business hours, including nights, weekends, and holidays when required for system upgrades or migrations.
• Required to sit or stand for long periods of time.
• The ability to lift 30-50 lbs without assistance.
• Local and/or international travel will be required as needed (10-15%) including some extended stays on location for education or deployments. Must have a valid driver's license and Passport.