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Director, Data Engineering

Dwyer Instruments Inc -
Remote

Apply Now

Job Details

Full-time
$165,000 a year

Qualifications

AI models
Commercial use (data warehousing systems)
Tooling
Infrastructure as Code (IaC)
Spark
Generative models
Managing IT teams
AI platforms (beyond public GPTs)
Scalable systems
Managing data teams
Cataloging
Infrastructure architecture design
Change management
SQL
Data Architecture Design (Architecture design skills)
Machine learning cloud services
Driver's License
Machine intelligence
Cloud database architecture
Managing engineering teams
Model deployment
Continuous integration
Cloud Native Design
Leading team collaboration initiatives
Scalability
Cloud automation
DevOps automation
Python
Generative AI
Data analytics technologies

Full Job Description

Remote Worker

Job Type

Full-time

Description

We are seeking a visionary Director, Data Engineering to architect the "data set of the future." This role is not just about reporting; it is about building the scalable, AI-ready infrastructure that will fuel our next generation of manufacturing innovation. You will move the organization beyond traditional data warehousing to a robust Data Lakehouse architecture, ensuring our enterprise data—from shop floor to point-of-sale—is clean, real-time, and ready for advanced GenAI and predictive modeling.

The ideal candidate is a technologist who fluently bridges the gap between the plant floor and the front office. You will be responsible for integrating complex operational data with high-velocity sales and commercial data to create a unified ecosystem. By connecting factory efficiency directly to customer demand and market trends, you will enable us to pivot from reactive operations to a truly predictive enterprise.

Key Responsibilities:

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.

Requirements

Qualifications and Technical Requirements:

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

Essential/Preferred Skills:

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.

Work Conditions and Physical Requirements:

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.

Salary Description

165,000

Apply Now

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