Data Engineer (Level II/Senior/Principal)

Benefits Overview:

Health, dental, 20 days PTO, 11 holidays, 12 weeks paid parental leave, stock options, professional development budget, and more.

Job Overview:

Found Energy is seeking a talented Data Engineer with a minimum of 2 years of industry experience to join our Research and Development team. The ideal candidate will have a background in implementing data infrastructure for both backend and frontend using the cloud. They will have an appreciation for the unique infrastructural needs of a startup gathering data over a rapidly increasing quantity of physical hardware systems, including chemical reactors with numerous sensor inputs. They will also be passionate about enabling our team to make data-driven insights and decisions that inform our processes and system designs. 

Having successfully developed core hydrogen generation technologies, including kW-scale power systems, Found Energy is now focused on scaling up reactor systems for both minimum viable product (MVP) and commercial pilot demonstrations. This endeavor involves designing and testing numerous sub-processes and sub-systems at industrial scale, as well as at various intermediate scales along the development path.

As a Data Engineer in this role, you will be responsible for implementing and owning robust data pipelines. You will design applications that run on these pipelines to monitor the performance of our reactors and supporting hardware, as well as generate diagnostic, descriptive, and predictive analytics for our commercial subsystems and routine bench-top experiments.

Responsibilities and Duties:

• Lead the development and maintenance of data infrastructure, including data pipelines, ETL processes, and databases, to support global operations across Found Energy's reactor locations. Implementation of solutions should follow iterative, lean software, and feature-driven development programming methodologies required in a fast-paced deep-tech startup. 
• Collaborate with interdisciplinary teams, including engineers and scientists, to design and implement applications for data analysis, dashboarding and real time monitoring, enabling stakeholders to derive insights and make informed decisions.
• Communicate with engineers and scientists to understand the fundamentals of our technology and translate these to data requirements, optimized data collection processes with a focus on expandability. 
• Own the management and optimization of storage solutions for large-scale experimental data, ensuring efficient retrieval and access through the use of custom API design and implementation.
• Drive programming best practices, employing the use of linters, intuitively named variables, unit and integration testing, CI/CD and auxiliary requirements documentation and technical design documentation.
• Model company values and participate in building and growing a positive and motivated company culture. Extend these values to our technical systems with a focus on enabling new insights, ensuring excellent documentation on infrastructure design and processing, and designing with data security in mind.

Preferred Qualifications:

• Academic degree in Computer Science or equivalent discipline, with a minimum of 2 years relevant experience. Certificate programs will be considered with relevant experience.
• Note: Role designation will be commensurate with experience and candidates with additional experience will be considered for advanced roles.
• Exceptional communication and documentation skills
• Flexibility to deal with the rapidly changing needs of a startup
• Deeply motivated by the climate crisis and its downstream impacts
• Experience in one or multiple of the following:
• Hands-on experience designing and maintaining data pipelines and ETL processes.
• Proficiency in working with large datasets and implementing scalable data storage solutions.
• Experience with SCADA systems and SQL programming.
• Experience working in Linux environments.
• Experience working with git and GitHub.
• Experience with common deployment tools like Docker.
• Experience with data analysis tools and frameworks employing Python, R, or Apache Spark.
• Knowledge of cloud platforms and services, such as AWS or Azure.
• Knowledge of implementing REST API.
• Experience with data visualization and UI tools, such as NodeRed.
• Familiarity with industrial automation systems and protocols.
• Working knowledge of regulatory standards and safety protocols relevant to data management in industrial settings.
• Experience analyzing time series data to extract common signatures and reveal feature correlations.