Director, Data Engineering

Semiconductor Insight

. Define and execute a data infrastructure roadmap centered on Lakehouse architecture .

Posted 11/10/2026full-timeRemote • Michigan • United StatesLead💰 $165,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data strategy and architecture, with a focus on Lakehouse architecture and operationalizing AI/ML models. Proven leadership in managing data teams and driving organizational change while ensuring data quality, security, and compliance.

Highest-signal resume keywords

Lakehouse Architecture DesignAI/ML Model OperationalizationData Governance FrameworksAzure Data Stack ProficiencyLeadership in Data Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSparkSQLCI/CD for Data PipelinesInfrastructure-as-CodeData Preparation for LLMsMLOps MethodologiesDataOps MethodologiesData CatalogingData Lineage

Soft Skills

Team BuildingMentoringCollaborationOrganizational Change Management

Tools & Technologies

DatabricksSnowflakeAzure Synapse/FabricADLS Gen2Azure AI

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceMaster’s Degree in Data Engineering

Industry Keywords

ManufacturingIndustrialECommerceIIoTPredictive Maintenance

Tech Stack

Tools & technologies

AzureCloudPythonSnowflakeSparkSQL

About the role

Key responsibilities & impact

Define and execute a data infrastructure roadmap centered on Lakehouse architecture
Integrate structured and unstructured data for real-time operational analytics and large-scale AI/ML workloads
Establish data governance, cataloging, and lineage frameworks for secure, trusted AI models and LLMs
Partner with OT and Engineering teams to ingest and operationalize IIoT and supply chain data
Create a unified data ecosystem supporting predictive maintenance and factory-floor efficiency
Lead the transition from legacy BI tools to modern, self-service analytics platforms
Lead adoption of MLOps and DataOps methodologies
Ensure data quality, security, and compliance in automated environments
Collaborate with business unit leaders to prioritize data products driving revenue growth or cost reductions
Build and mentor a team of data engineers, ML engineers, and data architects
Bridge cloud-native environments with complex legacy manufacturing systems

Requirements

What you’ll need

15+ years in data strategy, architecture, and engineering
At least 5 years in a leadership role driving organizational change
5+ years managing data and analytics teams
Demonstrated experience designing and deploying Lakehouse architectures at scale, such as Databricks, Snowflake, or similar
Proven experience operationalizing AI/ML models in an enterprise environment
Deep understanding of data preparation for LLMs and generative AI
Extensive experience with Azure or equivalent cloud hyperscaler data stacks, including Synapse/Fabric, ADLS Gen2, and Azure AI
Advanced proficiency in Python, Spark, and SQL
Strong experience with CI/CD for data pipelines and infrastructure-as-code
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field
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 core business hours, including nights, weekends, and holidays, when required
Ability to sit or stand for long periods
Ability to lift 30–50 lbs without assistance
Valid driver’s license and passport
Ability to travel locally and internationally as needed

Benefits

Comp & perks

Fully remote work arrangement
Local and/or international travel as needed (10–15%), including some extended stays on location for education or deployments