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Overview
Wynd Labs is seeking a Data Engineer to build and maintain data pipelines for their innovative project, Grass, which aims to make public web data accessible for AI. Competitive salary and equity offered. Wynd Labs is an early-stage startup that is on a mission to make public web data accessible for AI through contributions to Grass. Grass is a network sharing application that allows users to share their unused bandwidth. Effectively, this is a residential proxy network that directly rewards individual residential IPs for the bandwidth they provide. Grass will route traffic equitably among its network and meter the amount of data that each node provides to fairly distribute rewards. In non-technical terms: Grass unlocks everyone's ability to earn rewards by simply sharing their unused internet bandwidth on personal devices (laptops, smartphones). This project is for those who lead with initiative and seek to challenge themselves and thrive on curiosity. We operate with a lean, highly motivated team who revel in the responsibility that comes with autonomy. We have a flat organizational structure, the people making decisions are also the ones implementing them. We are driven by ambitious goals and a strong sense of urgency. Leadership is given to those who show initiative, consistently deliver excellence and bring the best out of those around them. Join us if you want to set the tone for a fair and equitable internet.
What you'll do
- •Designing, building, and optimizing scalable data pipelines to process and integrate data from various sources in real-time or batch modes.
- •Developing and managing ETL/ELT workflows to transform raw data into structured formats for analysis and reporting.
- •Integrating and configuring database infrastructure, ensuring performance, scalability, and data security.
- •Automating data workflows and infrastructure setup using tools like Apache Airflow, Terraform, or similar.
- •Collaborating with data scientists, analysts, and other stakeholders to ensure efficient data accessibility and usability.
- •Monitoring, troubleshooting, and improving the performance of data pipelines and infrastructure to ensure data quality and flow consistency.
- •Working with cloud infrastructure (AWS, GCP, Azure) to manage databases, storage, and compute resources efficiently.
- •Implementing best practices for data governance, data security, and disaster recovery in all infrastructure designs.
- •Staying current with the latest trends and technologies in data engineering, pipeline automation, and infrastructure as code.
Conditions
- •Opportunity to work at the forefront of developing a web-scale crawler and knowledge graph that allows ordinary people to participate in the process, and share in the benefits of AI development.
- •Culture of a lean team working together to achieve a very ambitious goal of improving access to public web data and distributing the value of AI to the people.
- •Competitive salary and equity package.