Operations Systems Engineer at Sila Nanotechnologies
Moses Lake, Washington, USA -
Full Time


Start Date

Immediate

Expiry Date

30 Nov, 25

Salary

181000.0

Posted On

31 Aug, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Git, Manufacturing Systems, Airflow, Python, Snowflake, Industrial Engineering, Data Engineering, Systems Modeling, Sql, Cmms, Mes, Models, Computer Science

Industry

Information Technology/IT

Description

ABOUT US

We are Sila, a next-generation battery materials company. Our mission is to power the world’s transition to clean energy. To create this future, our team is building a better lithium-ion battery from the inside out today. We engineer and manufacture ground-breaking battery materials that significantly increase the energy density of batteries, while reducing their size and weight. The result? Smaller more powerful batteries that can unlock innovation in consumer devices and accelerate the mass adoption of electric cars to eliminate our dependence on fossil fuels. We’re tackling one of the biggest challenges of our time every day, and together we’re redefining what’s possible. Are you ready to be a part of a team committed to changing the world?

KNOWLEDGE AND SKILL REQUIREMENTS

  • Bachelor’s degree in Industrial Engineering, Computer Science, Operations Research, or a related technical discipline.
  • At least five years of experience in systems modeling, data engineering, or applied decision science.
  • Demonstrated ability to build tools and models from early prototypes to modular, production-ready systems.
  • Proficiency in Python and SQL with experience using Git and collaborative software development workflows.
  • Hands-on experience with cloud data platforms such as Snowflake or similar.
  • Strong communication and cross-functional collaboration skills.
  • Comfort navigating ambiguity and applying structured, practical thinking to open-ended operational problems.
  • Master’s degree in a technical or applied field preferred.
  • Experience with optimization modeling, simulation frameworks, or automation systems.
  • Familiarity with manufacturing systems including MES, ERP, and CMMS.
  • Exposure to modern data engineering tools such as dbt, Prefect, Airflow, or containerized environments.
Responsibilities
  • Develop a deep understanding of process logic, decision frameworks, and operating models across manufacturing and business functions.
  • Work directly with domain experts to understand how decisions are made, and bring in the right data to structure models and tools that reflect that logic.
  • Own the full lifecycle of models, tools, or software from MVP to scaled deployment. Build alongside stakeholders, demonstrate value early, and iteratively refine solutions into durable systems.
  • Design with modularity and reusability in mind. Ensure that once a function is automated through logic and data, it becomes faster, cheaper, and easier to deploy in future factories or business contexts.
  • Design with the customer in mind, but recognize that when building foundational systems, initial requirements are only a starting point. You are responsible for innovating beyond what is asked, exploring what is possible, and delivering solutions that anticipate future needs and not just the current ones.
  • Refactor repeatable logic into clean, maintainable modules that significantly reduce development time for future projects.
  • Prioritize business impact and clarity over abstraction. Deliver useful tools quickly, then scale them thoughtfully in the background.
  • Guide the team in choosing the right technology stack and architecture based on the complexity of the data and the intent of the tool. Make clear decisions between building one-off solutions and creating mission-critical systems.
  • Collaborate closely with enterprise data and IT teams to ensure infrastructure, data governance, and integrations align with system architecture.
  • Apply automation and AI frameworks thoughtfully to recurring operational problems, such as supplier quoting, master data normalization, or document-based workflows. Prioritize transparency and explainability in all solutions.
  • Maintain model repositories, data pipelines, and supporting code using best practices in version control, collaboration, and performance monitoring.
  • Lead documentation, reviews, and improvement cycles to ensure all systems are scalable, reliable, and well-understood by the broader organization.
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