Senior Staff Software Engineer, Data

Lumilens•San Jose, CA

About The Position

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 industry’s veterans who’ve built and scaled some of the most transformative technologies in the industry. This isn’t incremental innovation, it’s a ground-floor opportunity to rethink the optical layer from the silicon up. 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. We’re looking for a Senior/Staff Software Engineer, Data to build the data pipelines and visualizations that turn raw manufacturing and test data into reliable signal for the engineers running our lines, the researchers pushing the technology forward, and the leadership team steering the company. You’ll work directly with optical, manufacturing, and quality/reliability engineers to design the pipelines, data models, and dashboards that help a fast-growing, multi-site organization make decisions based on trustworthy data.

Requirements

5+ years of professional software engineering experience, with a substantial portion spent building and owning data systems such as data engineering, analytics engineering, or backend systems handling large operational datasets. We are hiring across Senior and Staff levels.
Strong SQL skills and experience designing, building, and maintaining data pipelines.
Hands-on experience building dashboards and visualizations with a BI or analytics framework such as Superset, Looker, Tableau, or similar. Direct experience with Apache Superset is a strong plus.
Proficiency with the Python data stack, including pandas and numpy, and visualization libraries such as matplotlib, seaborn, altair, or similar.
Experience with cloud data infrastructure, preferably AWS.
Track record of partnering directly with non-data domain experts to turn ambiguous requirements into concrete, reliable data solutions.
Comfort owning a system end-to-end, from data model through dashboard, in a fast-moving startup environment.

Nice To Haves

Experience with manufacturing, semiconductor, hardware, or other high-volume operational or test data.
Familiarity with statistical process control, SPC, or yield analysis concepts.
Experience working across distributed, multi-site teams.

Responsibilities

Architect and build the data pipelines and infrastructure that turn manufacturing and test data, including yield, SPC, failure, and reliability data, into dependable signal for engineering and leadership decisions.
Design, build, and maintain dashboards and visualizations for stakeholders across the executive team, internal engineering, and external customers.
Partner directly with optical, manufacturing, and quality/reliability engineers to translate ambiguous domain requirements into robust data models and tooling.
Build and maintain automated data quality checks and validation pipelines, including checks that gate critical processes such as shipping readiness.
Identify and close data gaps, including missing, inaccurate, or inconsistent data, in close collaboration with domain experts and other data/ML engineers.
Drive improvements to pipeline reliability, turnaround time, and cost across product lines.
Apply strong software engineering practices, including version control, testing, code review, and CI/CD, to data systems, not just analysis scripts.
Help set technical direction for the data organization as it scales across sites in Thailand, Bangalore, Singapore, and the United States, and mentor other engineers on the team.