Data Engineer

Wynd Labs is an early-stage startup focused on making public web data accessible for AI through their Grass application. The Data Engineer will be responsible for building and maintaining data pipelines, optimizing data systems, and ensuring seamless data flow to support the company's mission of data-driven innovation.

Responsibilities:

• 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

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related technical field
• Extensive experience with database systems such as Redshift, Snowflake, or similar cloud-based solutions
• Advanced proficiency in SQL and experience with optimizing complex queries for performance
• Hands-on experience with building and managing data pipelines using tools such as Apache Airflow, AWS Glue, or similar technologies
• Solid understanding of ETL (Extract, Transform, Load) processes and best practices for data integration
• Experience with infrastructure automation tools (e.g., Terraform, CloudFormation) for managing data ecosystems
• Knowledge of programming languages such as Python, Scala, or Java for pipeline orchestration and data manipulation
• Strong analytical and problem-solving skills, with an ability to troubleshoot and resolve data flow issues
• Familiarity with containerization (e.g., Docker) and orchestration (e.g., Kubernetes) technologies for data infrastructure deployment
• Collaborative team player with strong communication skills to work with cross-functional teams