Apply in 3 Minutes! Data Analytics Engineer

About Lumilens

At Lumilens we are building the critical photonics infrastructure that powers tomorrow’s AI supercomputing. From chip-to-chip optical interconnects to scalable photonic engines, Lumilens is unlocking a new era of computing—faster, cooler, and massively more efficient.

We’re a well-funded startup backed by Mayfield and led by veterans who’ve built and scaled some of the most transformative technologies in the industry. At Lumilens, we’re developing high-speed photonics products purpose-built to power the future of AI infrastructure and high-performance computing.

This isn’t incremental innovation—it’s a ground-floor opportunity to rethink the optical layer from the silicon up. The market is moving fast, and we’re moving faster. You’ll work alongside a team of world-class engineers solving some of the hardest challenges in optics, systems, and scale. Every line of code, every design decision, every breakthrough you help deliver—will shape the infrastructure of tomorrow. If you're looking for mission, momentum, and the chance to make an outsized impact—jump on the rocket ship. We’re just getting started.

Position Overview

We are looking for an experienced analytics engineer to build the data foundations, define key metrics and make recommendations to guide strategic decisions and influence the trajectory of photonic research at Lumilens.

Analytics engineer

• Understand user requirements from a variety of domain experts and translate that to data tooling requirements
• Create rapid analytics prototypes (notebooks, dashboards) to address user needs
• Create dashboards and reports for stakeholders including exec team, internal users and external customers using structured and semi-structured datasets
• Identify data gaps (missing data, inaccurate data, inconsistent fields etc) and ensure improvements, working in close collaboration with domain experts (optical engineers, manufacturing engineers, quality / reliability engineers) and data and machine learning engineers
• Implement data quality checks
• Identify opportunities to improve timeline, reliability and costs for our product lines
• Follow good software engineering practices
• Core requirements : Dashboarding tools, Data visualization tools (eg matplotlib, seaborn, altair, Strong SQL skills, Python data stack (eg. pandas, numpy).

What we offer