Senior Data Engineer
Aircapture
| Company | Aircapture |
| Category | Engineering |
| Location | Berkeley |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 8 May 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
At Aircapture we’re creating technology to solve what we believe to be our lifetime’s most pressing challenge: the climate crisis. We supply commercial and industrial customers with clean CO₂ captured from our atmosphere to radically improve the environment, our economy, and our lives. We value building a team of people who represent diverse backgrounds—be it through education, gender, ethnicity, age, sexual orientation—to reach our goals. Thank you for considering us.
You will shape the data foundation that runs our technology by owning the pipelines, models, and analyses that turn operational data from our Direct Air Capture (DAC) systems into the insights that our engineers and scientists rely upon. Partnering with our test and engineering teams, you’ll turn the continuous stream of sensor, process, and test data from the physical equipment in the field and shop into actionable decisions. You have built and run production data systems in a hardware or physical product environment, and are comfortable with the realities of real-world sensor data, making durable architectural choices that hold up over time. If this sounds like you and you are excited to have a major impact at a groundbreaking climate technology startup, we want to hear from you!
This role is onsite at our development and production facility in Berkeley, California.
Salary: $180,000-$220,000 per year
Please include a cover letter with your application–describe why you are interested in this role and Aircapture using your own words (not AI).
What You’ll Do Here
Own and evolve Aircapture’s end-to-end data platform, using both Ignition and AWS
Set technical direction for the data stack, evaluating tools, defining standards, and making durable architectural decisions
Standardize and enforce engineering workflows across the data stack, such as version control, code review, CI/CD, testing, and documentation
Maintain and support on-prem server systems as part of the data infrastructure
Partner closely with controls engineering on Ignition rollout
Partner with test engineering to build the pipeline that moves test and operational telemetry, including sensor, DAQ, and historian data, into the dashboards and reports engineering relies on
Your Skills and Experience Include
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent hands-on industry experience, Master’s preferred
6+ years building and operating production data systems that are mission critical to a hardware or physical product, gained in an industry such as automotive, aerospace, robotics, industrial equipment, energy, or semiconductor manufacturing. Hands-on hardware/physical-product experience required
Solid experience with AWS and services such as RDS, IAM, VPC, S3
Strong SQL and Python expertise combined with solid engineering fundamentals across Git, CI/CD, and production workflows
Thorough background in dbt across the full model lifecycle, plus orchestration tools like Airflow and Docker for production deployment
Hands-on experience with industrial historian or DAQ platforms such as Ignition, ibaPDA, Keysight, National Instruments, or similar, as well as fluency working with time-series and sensor data
Working physical intuition for the systems generating your data — thermal, fluid, electrical, or mechanical — sufficient to sanity-check sensor readings, spot anomalies, and communicate credibly with test and controls engineers
Strong analytical orientation toward data interpretation, with the ability to navigate ambiguity and adjust architectural decisions accordingly
Effective cross-functional collaborator and communicator; able to translate ambiguity into scoped work and push back when needed
Self-directed, resourceful, and highly ownership-oriented from scoping t
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