Data & Platform Engineer

Spectrum Control•Fairview, PA

7d•Hybrid

About The Position

We are looking for a visionary and hands-on Data & Platform Engineer to lead enterprise data and platform modernization including AI initiatives as part of our broader digital transformation strategy. This role will design, implement, and govern secure and intelligent data platforms that connect manufacturing, business, and cloud systems to drive operational insights, automation, and innovation. You will play a critical role in enabling our organization to make data-driven decisions by bridging traditional manufacturing processes with modern digital capabilities in a regulated, security-conscious environment.

Requirements

Degree in Computer Science, Information Systems, Data Engineering, or related field.
5+ years of experience in enterprise data architecture and analytics, with at least 2 years in a manufacturing environment.
Demonstrated success driving digital transformation initiatives using modern cloud, AI, and data platforms.
Hands-on experience with SQL, specifically SQL 2016, SSIS, data modeling, tools, and data pipeline orchestration.
Working knowledge of cybersecurity and compliance frameworks (e.g., NIST 800-171, CMMC, ITAR).

Nice To Haves

Experience with Azure IaaS/SaaS, Power BI, and Microsoft Fabric.
Experience integrating factory systems with enterprise platforms.
Certifications: Azure Data Engineer or related AI/Cloud credentials.
Experience with modernizing traditional infrastructure with Cloud platforms.
Strategic thinker with a strong sense of operational urgency and pragmatism.
Proven ability to communicate technical topics to non-technical stakeholders.
Comfortable leading across functions in manufacturing, operations, quality, and IT.

Responsibilities

Data Modeling - Maintain and evolve the current data warehouse structures, including tables, schemas, and relationships, ensuring they support reporting and analytics needs.
ETL processes - Support, optimize, and enhance ETL/ELT pipelines to ensure reliable, timely ingestion from multiple source systems.
Modernization & Optimization - Tune queries, indexes, and storage strategies for optimal speed, scalability, cost efficiency and leverage AI to boost activities. Focus on reducing technical debt, improving performance, enabling scalability, and ensuring cost efficiency.
Data Governance & Security - Enforce governance policies, data quality standards, and security controls to meet compliance and regulatory requirements.
Collaboration - Partner with business users, analysts, and application teams to understand requirements and deliver actionable, trusted data.
Future Proofing the Enterprise – Creating a resilient, flexible platform that can adapt to evolving technologies, customer demands, and regulatory requirements, while positioning the organization for continuous digital innovation. Conducting comprehensive evaluations of current legacy systems, data platforms, and infrastructure to identify inefficiencies, risks, and modernization opportunities.
GenAI Enablement for Manufacturing Build and maintain secure data pipelines that feed machine learning models for predictive maintenance into factory floor systems. Collaborate with analytics teams to operationalize AI models for real-time decision-making. Evaluate and integrate AI platforms such as Azure CoPilot, ChatGPT, Salesforce AI, Microsoft Fabric and others into enterprise workflows. Using machine learning to transform our data into actionable insights that drive business performance by combining expertise in computer science, mathematics, and statistics.
Data Architecture & Integration Manage an enterprise-wide data architecture that integrates our legacy ERP (as400/KBM) and factory floor systems. Support and improve data models to support KPIs across various departments. Lead the migration of legacy data environments to scalable cloud platforms (Azure, Power BI, Tableau, Snowflake, and others.).
Cybersecurity & Compliance Ensure all data systems comply with CMMC, NIST 800-171, ITAR, and EAR regulations through design and access control. Implement data protection strategies such as role-based access, encryption, logging, and secure APIs across platforms. Work closely with the cybersecurity squad to support secure DevOps (DevSecOps), data classification, and audit readiness.
Cloud Strategy Drive the use of hybrid and edge computing for manufacturing data acquisition and analysis. Enable high-speed analytics for machine and process data at the edge while syncing to enterprise cloud for historical insight. Support real-time telemetry integration from factory devices and smart sensors.
Collaboration & Governance Serve as an architecture advisor to data engineers, management, stakeholders, and analysts. Establish data governance practices, including data quality standards, system lifecycle policies, and documentation. Support Agile delivery of data and analytics features across tribes and squads. Participate in strategic planning and roadmap development and enterprise systems.