Byterat is a modern data platform for battery science. We provide battery teams at companies like Panasonic or Tesla with the core data platform they need to innovate, securely at scale. Byterat runs in the background of a battery lab, and enables scientists to analyze thousands of parallel experiments at once to connect the dots between battery design and performance.

About Byterat

Byterat is the modern data platform for battery science. We give battery teams at companies like Panasonic or Tesla the core data platform they need to innovate, securely at scale.

We’re a well-funded, VC-backed start-up with customers and recurring revenue. We’re backed by world-class investors like Y Combinator, Giant Ventures and Collaborative Fund, and angels such as founders of Zendesk and Voi, and executives from Google, Meta and Figma. 

We’re growing rapidly, which presents career-defining opportunities for ambitious engineers to accelerate their growth and contribute to a quickly evolving startup in SF. You’ll be joining our in-person team of engineers and physicists with backgrounds from Rivian, Carnegie Mellon, Georgia Tech, Shopify, U. Cambridge and other YC start-ups.

Our Mission

• Batteries play a key role in tackling climate change. $73B was invested in US battery plants in 2022, and battery company revenue is expected to grow >5x over the next decade.
• Global carmakers alone are forecast to spend $515B on electric vehicle and battery R&D by 2030. But battery engineers are woefully underserved by software, relying on broken legacy products to analyze their data. Most data is never analyzed or used meaningfully.
• Byterat is the first platform to provide battery teams with the infrastructure they need to unlock business value from their battery data. Byterat is live in battery labs across three continents today, allowing teams to analyze data from thousands of parallel experiments and unlock previously hidden insights connecting battery design to performance.

We might be a fit for you if…

• You’re ambitious and you want to move fast. You want to accelerate your trajectory and have an outsized impact on a rapidly growing VC-backed startup in SF.
• You’re hard-working. You’re a team player. You can communicate effectively and respectfully. You pay attention to details. You like to solve the most important problems and you’re good at doing that. You adapt quickly.
• You want to join a strong, supportive, high-performance team, and work in-person in SF. You set an exceptionally high performance bar for yourself and everyone on the team.
• You are not afraid to communicate what is working and what needs to improve.
• You thrive in a fast-paced environment where you’re given autonomy to build core features.

Qualifications

• Education: Bachelor's degree in Data Science, Engineering, Computer Science, or a related field.
• Experience: 1-4 years of experience in data engineering and visualization, focusing on actionable insights.
• Technical Skills: Proficiency in SQL, Python, and experience with DBT, Airflow, Mage, and Git.
• Cloud Technologies: Experience with cloud services like S3, ECS, EMR, Lambdas, and Glue.
• ETL & Data Engineering: Background in ETL processes, data modeling, and data engineering best practices.
• Elasticsearch: Exposure to Elasticsearch for indexing, querying, and managing large datasets.
• Time Series Data: Familiarity with time series data and tools such as PostgreSQL, Druid, TimescaleDB, and InfluxDB is a plus.
• Machine Learning: Understanding of integrating machine learning models into data workflows to extract meaningful insights.
• Visualization Expertise: Skills in creating impactful visualizations.
• Communication: Strong communication and collaboration abilities, with a focus on meeting client needs.

Responsibilities

• Assist in building and maintaining data pipelines, transforming data from multiple sources into our data lakes.
• Contribute to the design, implementation, and management of data models and workflows, ensuring scalability and performance.
• Help integrate data from diverse sources, including both structured and unstructured data, into cohesive datasets.
• Use tools like DBT, Airflow, and Mage to transform and load data into analytics platforms.
• Support the automation of data processing and visualization updates using Managed Airflow or similar tools.
• Data Quality Assurance: Implement monitoring tools like DataDog, CloudWatch, Monte Carlo, and the ELK stack to ensure data accuracy and reliability.
• Assist in developing and managing Elasticsearch clusters for data indexing and search capabilities.
• Work closely with clients to understand their needs and deliver tailored data solutions.
• Contribute to the design of advanced data tools and visualizations to help clients understand and optimize their battery technologies.
• Collaborate with data scientists to integrate machine learning models and predictive analytics into our data ecosystem, providing actionable insights into battery performance and longevity.

Our tech stack

• Postgres database with data indexing using ElasticSearch
• Modern python based ETL with a node.js / next.js GraphQL layer 
• React TypeScript front-end
• Hosted in AWS with Kubernetes

Postgres database with data indexing using ElasticSearch Modern python based ETL with a node.js / next.js GraphQL layer React TypeScript Front-end Hosted in AWS with Kubernetes