Staff R&D Data Engineer, Hardware Systems

About the role

Lydian is seeking a Staff R&D Data Engineer to build the core data infrastructure for our technical operations. As the company scales, you will manage the increasing volume of experimental, manufacturing, and quality data. This role is crucial for accelerating learning and improvement across our hardware systems.

Key facts

What you'll do

• Design and maintain data infrastructure, ensuring consistent storage and easy access for experimental, manufacturing, and quality control data.
• Develop analysis and visualization tools, including automated summary statistics and performance metrics for R&D and pilot experiments.
• Program and maintain data acquisition software for R&D and pilot test equipment, ensuring reliable data streaming and quality control checks.
• Conduct statistical and regression analyses to connect manufacturing and QC data with experimental run data, uncovering cross-functional insights.
• Collaborate with scientists and engineers to understand their data requirements, translating them into effective tools and workflows, and support IT infrastructure.

Requirements

• BS/MS degree in engineering, physical science, computer science, data science, or a related field.
• 10+ years of experience with data in a hardware, lab, or physical science R&D setting (software-only experience is not applicable).
• Demonstrated capability across the entire data lifecycle: acquisition, infrastructure, pipelines, visualization, and statistical analysis.
• Proven history of establishing or enhancing data storage and analysis infrastructure.
• Expert proficiency in SQL and Python (including pandas/NumPy, query optimization, and schema design).
• Experience handling time-series and sensor data, including alignment, gap-filling, and reconciling schema drift.
• Ability to perform statistical/regression analysis on experimental data and create clear visualizations and dashboards.
• Proficiency with at least one DAQ platform (LabVIEW, DAQ Factory, or Ignition).
• Fluent in using AI coding assistants for scaffolding and debugging, with the ability to validate their output.
• Strong communication skills and the ability to work independently as the most senior data professional on the team.

Nice to have

• Background in chemical, materials, or mechanical engineering.
• Familiarity with modern data stack tools (Airflow, Dagster, dbt, Snowflake/DuckDB/Iceberg).
• Experience with automated data testing (Great Expectations, dbt tests).
• Experience with Notion, Nominal, or JMP.
• Familiarity with additional DAQ platforms.
• Basic IT and networking skills.

Skills & tools

• SQL
• Python (pandas, NumPy)
• DAQ Factory
• Ignition
• Nominal
• Notion
• JMP
• LabVIEW
• Airflow
• Dagster
• dbt
• Snowflake
• DuckDB
• Iceberg
• Great Expectations

Practical notes

This role offers competitive compensation, meaningful equity, generous paid time off, and excellent insurance coverage (100% for employees, 80% for dependents). A 401(k) with company match is also provided.