Data Systems & Industrial Automation Engineering Supervisor

Experienced and results-driven engineering supervisor with a strong background in data systems architecture, database management, and data quality engineering, combined with hands-on knowledge of industrial automation technologies. Proven ability to lead cross-functional teams in the development and deployment of scalable data infrastructure and automation solutions that support manufacturing excellence and digital transformation.

What you'll do:

• Supervise and mentor a team of engineers and analysts responsible for data infrastructure and automation system integration.
• Oversee the design, implementation, and maintenance of data warehouses, ensuring alignment with manufacturing system requirements.
• Lead data cleansing and standardization efforts to improve data quality across automation platforms.
• Collaborate with controls and software teams to integrate hardware and software systems for real-time data acquisition and control.
• Ensure compliance with cybersecurity, data governance, and industrial communication protocols.
• Drive continuous improvement through KPI tracking, root cause analysis, and system optimization.
• Support strategic initiatives in smart manufacturing, digital twins, and predictive analytics.

Data Systems & Quality

• Data Accuracy Rate: % of clean, validated data in warehouse systems
• ETL Pipeline Reliability: % uptime and successful job execution
• Data Warehouse Query Performance: Average response time for key queries
• Data Cleansing Throughput: Volume of records cleansed per month

Automation Integration

• System Integration Success Rate: % of automation systems integrated without major rework
• Automation Uptime: % availability of integrated control systems
• Time to Deploy New Automation Features: Average time from development to production

Team & Project Management

• Feature Completion Rate: % of planned features delivered per sprint
• Team Productivity Index: Tasks completed vs. planned
• Stakeholder Satisfaction Score: Feedback from internal customers and partners
• Compliance Rate: % of systems meeting cybersecurity and industrial standards

Technical Expertise

• Database Technologies: SQL Server, PostgreSQL, MongoDB, Snowflake
• Data Engineering: ETL, data pipelines, data modeling, cleansing tools (e.g., Talend, Alteryx)
• Automation Systems: PLCs (Siemens, Allen-Bradley), SCADA, HMI, OPC UA
• Programming & Scripting: Python, SQL, PowerShell
• Tools & Platforms: Ignition, Kepware, Google Data Factory, Power BI
• Leadership: Team supervision, project management, cross-functional collaboration
• Standards & Compliance: ISA-95, ISA-88, NIST Cybersecurity Framework
• Agile Methodologies (Scrum, Kanban)
• Software Development Life Cycle (SDLC)
• Continuous Integration/Continuous Deployment (CI/CD)
• Code Review and Quality Assurance
• Problem Solving and Critical Thinking
• Excellent Communication and Collaboration
• Cloud-Based Data Applications
• Mobile Application Development (Google and Apple Platforms)
• Customer-Centric Application Development

Minimum Qualifications:

• Bachelor’s degree in Electrical Engineering, Computer Engineering, or related field (Master’s preferred)
• 5+ years of experience in data systems and industrial automation
• 7+ years of experience in database systems, including SQL/NoSQL, data warehousing, and ETL pipelines
• 3–5 years of experience working with industrial automation systems, including PLCs, SCADA, robotics, and embedded control systems

Preferred Qualifications:

• Proven leadership in engineering or technical team environments
• Strong understanding of manufacturing processes and digital transformation initiatives
• Excellent communication, problem-solving, and organizational skills
• Demonstrated leadership in d