Senior R&D Data Engineer, Hardware Systems

About the role

You will design and maintain the data backbone that turns complex hardware experiments into structured, queryable knowledge for the entire R&D organization. You will own the flow of experimental run data from noisy sensors into clean, analysis-ready tables that accelerate learning cycles. In this role you will build the dashboards and analysis tools that allow scientists and engineers to ask and answer questions without waiting for software-only support. You will act as the primary data generalist bridging process engineering, laboratory operations, and software infrastructure. You will ensure that every data point, from voltage readings to QC flags, is captured with context and can be traced back to the exact conditions of the run. You will partner directly with the VP of R&D to define standards that make our growing data estate reliable and scalable. You will be the senior data professional on the R&D team, responsible for both the architecture and the day to day trustworthiness of our analytical systems.

Key facts

What you'll do

Design and maintain data infrastructure that stores experimental run data, run metadata, manufacturing records, and QC information across Nominal, Notion, and evolving systems.

Build analysis and visualization tools such as run summary statistics and performance metrics that let the team analyze results faster and with consistent definitions.

Program and maintain data-acquisition software including platforms like DAQ Factory and Ignition so that lab and pilot equipment streams reliably into centralized databases.

Establish data validation checks at acquisition and entry to catch instrument errors, schema drift, and alignment issues before they corrupt analysis.

Perform cross-functional statistical and regression analyses that connect manufacturing and QC data with experimental run data to reveal process insights.

Partner with scientists and process engineers to translate data needs into reliable workflows, clear plots, dashboards, and automated reporting.

Carry out end to end ownership of data quality, from raw sensor streams through curated tables that support decision making across R&D.

Translate requirements from hardware and process teams into database schemas, time-series handling strategies, and robust pipeline designs.

Maintain the IT infrastructure for servers, databases, and analysis environments that house the data backbone supporting all technical teams.

Enable traceability by ensuring every dataset documents its origin, processing steps, and quality checks so future experiments can build on provenance.

Continuously improve data documentation and accessibility so that new team members can find and understand historical experiments without extensive handholding.

Champion best practices in schema design, query optimization, and automated testing to keep the data system performant as volumes grow.

Requirements

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

5+ 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 including acquisition, infrastructure, pipelines, visualization, and statistical analysis.

Proven track record establishing or improving data storage and analysis infrastructure in demanding technical environments.

Expert level SQL and Python proficiency with pandas, NumPy, query optimization, and thoughtful schema design.

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

Statistical and regression analysis capability on experimental data with the ability to turn results into clear visualizations and dashboards.

Proficiency with at least one data acquisition platform such as LabVIEW, DAQ Factory, or Ignition.

Fluency using AI coding assistants to scaffold and debug pipelines and SQL, with strong judgment to validate their output for correctness and safety.

Strong communication skills to work independently as the team's senior-most data professional and to clearly explain complex analyses to non-technical stakeholders.

Nice to have

Background in chemical, materials, or mechanical engineering to better understand the scientific and manufacturing context of the data.

Experience with modern data stack tools such as Airflow, Dagster, dbt, and data platforms like Snowflake, DuckDB, or Iceberg.

Practical knowledge of automated data testing frameworks like Great Expectations or built-in dbt tests to catch data quality issues early.

Familiarity with Notion, Nominal, or JMP for documentation, experiment tracking, or statistical analysis.

Additional experience with other DAQ platforms beyond those explicitly required.

Practical notes

This is a full time position based in Boston.

Employment is at will.

Travel is not expected at this time.

Visa sponsorship is not available for this role at this time.

Candidates must be authorized to work in the United States without sponsorship.

No specific deadline is published for this role.