Company Overview

Lydian is developing the lowest cost sustainable fuels from waste CO2, water, and renewable electricity to decarbonize the aviation industry. Our breakthrough technology replaces oil and gas refining with fully electrified, modular, and flexible reactor systems that produce fuel with 95% lower emissions than traditional jet fuel.

Since our founding in 2021, Lydian has achieved key milestones in record time, including the production of our first liquid fuel from our pilot system, capable of producing ~10,000 gallons of fuel per year for a fraction of the capital and time of alternative approaches. Lydian is backed by top climate investors including Congruent Ventures, Galvanize Climate Solutions, Union Square Ventures, Voyager Ventures, Grok Ventures, and Overture. We are building a motivated, diverse, and collaborative team that is passionate about addressing the growing climate crisis and is committed to excellence in their work. In particular, we look for tenacious people who are energized by solving the hardest, highest impact problems that come their way.

Position Overview

Lydian is looking for an R&D Data Engineer to build and own the data backbone of our technical organization. As we scale from pilot operations toward commercial production, our teams are generating a growing volume of experimental, manufacturing, and quality data. The ability to store, connect, and analyze that data well has become central to how quickly we can learn and improve. In this role you will develop our data infrastructure, build the analysis and visualization tools our engineers and scientists rely on, and carry out statistical analyses in support of the entire technical team.

This is a data generalist role at the intersection of hardware and software. You should be equally at home designing a database schema, writing Python to wrangle time-series data streaming off lab instruments, building a clear plot or dashboard, and talking through an analysis with a process engineer. You will report to the VP of R&D and work closely with the full R&D team as well as our mechanical and process engineering groups.

Responsibilities

• Design and maintain data infrastructure. Establish structured, systematic data storage across our tools (currently Nominal and Notion, plus others as needs evolve). Ensure experimental run data (time series), run metadata, and manufacturing and QC data are integrated, consistently formatted, and easy to sort, filter, and query for analysis

• Build analysis and visualization tools. Create the data views, plots, and statistical tools that help the team analyze data faster and better. For example, this will include standardizing and automating run summary statistics and overall performance metrics across R&D and pilot experiments

• Enable laboratory data acquisition. Program and maintain data-acquisition software (e.g., DAQ Factory, Ignition) so that R&D and pilot test equipment stream reliably into our centralized databases. Build in data validation checks that quality-control data at the point of acquisition or entry

• Run cross-functional analyses. Perform statistical and regression analyses that connect manufacturing and QC data with experimental run data to surface insights across functions

• Partner across the technical team. Work with scientists and engineers to understand their data needs and translate them into reliable tools and workflows, and help maintain the IT infrastructure of servers and software systems that house our data

Qualifications

• BS/MS in engineering, physical science, computer science, data science, or related field

• 10+ years experience with data in a hardware, lab, or physical-science R&D environment (software-only experience does not qualify)

• Demonstrated strength across the full data lifecycle: acquisition, infrastructure, pipelines, visualization, and statistical analysis

• Proven track record establishing or improving data storage and analysis infrastructure

• Expert level SQL and Python proficiency (pandas/NumPy, query optimization, schema design)

• Experience handling time-series and sensor data; alignment, gap-filling, and reconciling schema drift across instrument runs and batches

• Statistical/regression analysis on experimental data, with the ability to turn it into clear visualizations and dashboards

• Proficiency with at least one DAQ platform (LabVIEW, DAQ Factory, or Ignition)

• Fluency using AI coding assistants to scaffold and debug pipelines and SQL, with judgment to validate their output

• Strong communication skills; able to work independently as the team's senior-most data professional

Preferred Qualifications

• Background in chemical/materials/mechanical engineering

• Modern data-stack tools (Airflow, Dagster, dbt, Snowflake/DuckDB/Iceberg)

• Automated data testing (Great Expectations, dbt tests)

• Experience with Notion, Nominal, or JMP

• Additional DAQ platform familiarity

• Basic IT and networking skills

Company Culture and Benefits

• Challenging, collaborative, and meaningful work and an important voice in company development

• Competitive salary commensurate with experience

• Meaningful equity compensation

• Generous PTO allowing employees to take time off to rest, recharge, and enjoy life outside work

• Excellent insurance with 100% of healthcare, vision, and dental premiums covered for the employee, and 80% coverage for dependents

• 401(k) with company match