Lead Scientific Data Engineer

Interested in working with a groundbreaking company that’s focused on decarbonizing the atmosphere? CarbonCapture Inc. develops and deploys direct air capture (DAC) machines that can be connected in large arrays to remove massive amounts of carbon dioxide from the atmosphere. With a patented modular open systems architecture, our DAC platform allows for plug-and-play upgrades, mass production, unlimited scalability, and rapid technology iterations.
We are looking for a motivated and passionate Lead Scientific Data Engineer to join our Systems and Analysis team. You will lead the continued development of our internal software infrastructure and collaborate with cross-functional groups to build data-driven solutions for a variety of technical challenges.
Our software and data infrastructure spans the full stack of our company: instrumentation modeling, control logic, data collection, data processing, data analysis, simulation, system modeling, and data visualization. Our scientific Python ecosystem (pandas, NumPy, and SciPy) is central to our work. We develop on and for Linux, containerize with Docker, and use AWS for deployment, data storage, caching, and web app hosting.
Your work will directly impact CarbonCapture’s ability to operate and learn from our systems, iterate and scale our technology, and remove CO2​to mitigate the effects of climate change.

Responsibilities

• Data Architecture: Own the vision, design, and implementation of our cloud-based architecture, ensuring it is scalable, reliable, and secure.
• Data Pipelines: Lead the development of robust, automated pipelines for collecting, processing, and analyzing high-volume time-series data from our DAC systems
• Technical Problem-Solving: Design and build novel solutions to data and analysis problems in system design, control, and analysis workflows. Contribute to the modeling and optimization of key system subprocesses.
• Data Tooling: Oversee the development and extension of data visualization and analysis tools (e.g., Dash, Plotly)
• Strategic Collaboration: Partner with our team of process engineers, material scientists, and system analysts to identify needs, document requirements, and drive solutions.

Requirements

• 5+ years of experience in data engineering, software engineering, or a related scientific computing role, with demonstrated technical leadership.
• Experience designing, building, and managing cloud-based data infrastructure and services (e.g., AWS, GCP, Azure).
• Strong proficiency with Python, Pandas, and NumPy.
• Proficiency with containerization (Docker) and version control workflows (Git).
• Comfort and proficiency with the Linux command line.

Core Competencies

• Strategic Thinking: Ability to make high-level design choices and dictate technical standards, considering long-term scalability and business impact
• Scientific Aptitude: Ability to grasp and apply scientific and engineering concepts, particularly in thermodynamics, test engineering, and material science
• Leadership and Ownership: Ability to lead projects from conception to completion, manage one’s own projects and priorities, and take full ownership of the data ecosystem.
• Communication: Contribute effectively to design discussions, documentation, and project tracking; collaborate with others to address potential roadblocks.

Desired Background

• Experience building scientific computation / data analysis tools, particularly in startup or R&D environments.
• Experience with a broad range of AWS services (e.g., S3, Lambda, Athena, Glue, EC2)
• Familiarity with or willingness to learn about industrial control systems and protocols (SCADA, Ignition HMI, PLC development)
• Proficiency in SQL and experience with various database technologies
• Experience or willingness to learn about deploying front-end applications for data visualization (e.g, Dash)

CarbonCapture Inc. is an equal opportunity employer, making all employment decisions based on merit, qualifications, and abilities. We recruit, hire, train, and promote without regard to race, religious creed, color, national origin, ancestry, physical or mental disability, medical condition, genetic information, marital status, sex, sexual orientation, gender identity and expression, age, military and veteran status, or any other protected characteristic under federal, state, or local laws.