SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Lydian is developing sustainable aviation fuels from waste CO2, water, and renewable electricity, with breakthrough technology that produces jet fuel with 95% lower emissions than traditional sources. The company has achieved key milestones since 2021, including producing liquid fuel from a pilot system capable of ~10,000 gallons annually, and is backed by top climate investors.
As Staff R&D Data Engineer, you will build and own the data backbone of the technical organization. You'll design and maintain data infrastructure across experimental, manufacturing, and quality data systems as Lydian scales from pilot to commercial production. This is a data generalist role at the intersection of hardware and software, requiring expertise across the full data lifecycle.
Key responsibilities include: designing and maintaining structured data storage across tools (Nominal, Notion, and others); building analysis and visualization tools including dashboards and statistical summaries; enabling laboratory data acquisition by programming DAQ software (DAQ Factory, Ignition) with validation checks; performing cross-functional statistical and regression analyses; and partnering with scientists and engineers to translate data needs into reliable tools and workflows.
You'll report to the VP of R&D and work closely with the full R&D team, mechanical engineers, and process engineers. Required qualifications include 10+ years in hardware/lab/physical-science R&D data work, expert-level SQL and Python (pandas/NumPy), experience with time-series and sensor data, proficiency with at least one DAQ platform, and strong statistical analysis skills. You should be equally comfortable designing database schemas, wrangling streaming instrument data, building dashboards, and discussing analyses with process engineers. Preferred experience includes chemical/materials/mechanical engineering background, modern data-stack tools (Airflow, Dagster, dbt, Snowflake), automated data testing, and familiarity with Notion, Nominal, or JMP.
