Manufacturing Design Engineer Internship, Skydio

• You’ll thrive if you have a mix of data engineering, analytics, and a practical understanding of manufacturing
• Currently pursuing a degree in Industrial Engineering, Manufacturing Engineering, Computer Science, Data Science, Electrical Engineering, or a related technical field
• Hands-on experience with SQL and Python — comfortable writing production queries and notebooks
• Familiarity with cloud analytics / data platforms (Databricks / Spark, Delta Lake, or similar)
• Understanding of MES concepts and shop-floor data (OEE, takt time, cycle time, yield) and how to map MES signals to production events
• Basic data engineering skills: data modeling, time-series handling, data quality tests and monitoring
• Ability to work on the factory floor to gather requirements and validate dashboards / data (comfortable in a production environment)
• Good communicator: able to partner across manufacturing, quality, supply chain, and software teams to define metrics and interpret results
• Strong problem solver: can take ambiguous manufacturing questions and produce reproducible data products and analyses
• Attention to detail and a documentation mindset: data contracts, lineage, and reproducible notebooks that others can run

Desirable

• As a Summer 2026 Manufacturing Systems Engineering Intern you’ll focus on the systems, data, and analytics that make production run: building and operating data pipelines, integrating MES and shop-floor data sources, and delivering operator-facing dashboards and analytic products that drive decisions across production, quality, and supply chain
• You’ll partner with manufacturing, quality, and software teams to translate shop-floor questions into reliable data products that surface root causes, monitor process health, and enable faster, more predictable production ramp
• Below are representative projects you may own. Each is an opportunity to turn raw production data into reliable insights that scale to full production
• Integrate MES / shop-floor data into a central analytics platform
• Deliverables: integration plan (sources, schemas, cadence), ETL/ELT pipelines ingesting MES / PLC / test-stand data into Databricks (or equivalent), automated data quality checks, and documented data model
• Build near real-time production dashboards for operators and supervisors
• Deliverables: high-level and station-level dashboards (Looker/Tableau/PowerBI or embedded web dashboards) showing KPIs (OEE, yield, cycle time, WIP, rejects), drill-downs to failed units, and operator-facing views. Include mockups, implementation notes, and a rollout plan
• Automate KPI calculations & alerts (OEE, first-pass yield, SPC)
• Deliverables: production-grade Databricks notebooks or jobs that calculate KPI time-series, SPC charts, automated anomaly detection/alerts, and unit-level traceability to identify defect clusters
• Build & validate dashboards for pilot builds and line expansion
• Deliverables: pilot build dashboards, rollout checklist, performance baselines, and documented validation showing that dashboards accurately reflect pilot outcomes