Data Engineer (Tech-lead)

Team: IT

Location: Brazil

Commitment: Full-time

Workplace Type: remote

Accountabilities:

• Design and architect scalable, reliable, and high-quality data systems and end-to-end pipelines following modern engineering standards and best practices.
• Lead the development of complex data models and transformation workflows using Snowflake and dbt.
• Design, implement, and optimize Apache Airflow workflows to orchestrate sophisticated data pipelines and manage complex dependencies.
• Establish reliable infrastructure and deployment practices using Docker, Kubernetes, and GitLab CI/CD.
• Take ownership of the complete data product lifecycle, from architecture and initial development through deployment, production operation, and continuous improvement.
• Develop and maintain robust data processing and automation solutions using Python, SQL, and Bash.
• Provide technical leadership, mentorship, and guidance to engineering team members, promoting strong development standards and engineering practices.
• Drive technical decision-making around architecture, scalability, reliability, maintainability, and production readiness.
• Collaborate closely with distributed technical and product teams to translate requirements into effective data solutions.
• Ensure data systems and products are delivered efficiently, reliably, and in alignment with broader project objectives.
• Participate in technical ceremonies, stand-ups, architecture discussions, and collaborative sessions during overlapping Central Time business hours.

Requirements

• Advanced proficiency in Python, SQL, and Bash, with the ability to apply these technologies to complex data processing, automation, and engineering challenges.
• Deep hands-on experience with Snowflake as a primary cloud data warehouse.
• Strong experience with dbt for data modeling, transformation, testing, and maintainable analytics engineering workflows.
• Practical expertise with Apache Airflow for designing, orchestrating, monitoring, and troubleshooting complex data workflows.
• Solid DevOps experience with Docker, Kubernetes, and GitLab CI/CD, including containerization, deployment automation, and environment consistency.
• Proven experience in data system architecture, with the ability to design scalable and production-ready data ecosystems.
• Previous experience leading or managing technical teams, providing mentorship and establishing engineering best practices.
• Strong understanding of the complete data product lifecycle, from initial architecture through production deployment and ongoing improvement.
• Excellent analytical and problem-solving abilities, with a structured approach to complex technical challenges.
• Strong communication and collaboration skills, particularly in distributed and cross-functional teams.
• Fluent English, with excellent written and verbal communication skills for interviews, technical documentation, daily collaboration, and team leadership.
• Experience building interactive data applications or dashboards with Streamlit is a plus.
• Experience designing automated data validation and data quality systems is desirable.
• Demonstrated success in shipping multiple data-centric products in fast-paced environments is an advantage.
• Availability to work remotely from Brazil with consistent overlap with Central Time (CST/CDT), including key meetings, stand-ups, and collaborative sessions.

Benefits

• Fully remote work from Brazil as part of an international LATAM-based team.
• Full-time contractor engagement with exposure to international technology projects and distributed engineering teams.
• Opportunity to take on a high-impact technical leadership role across architecture, data engineering, DevOps, and product delivery.
• Hands-on work with modern data technologies including Snowflake, dbt, Apache Airflow, Docker, Kubernetes, and GitLab CI/CD.
• High level of technical ownership over the design, development, and production delivery of data products.
• Opportunity to collaborate with diverse international teams and solve complex data engineering challenges.
• Professional growth through technical leadership, architecture ownership, and exposure to modern data and cloud engineering practices.
• Remote collaboration aligned with Central Time business hours, providing structured overlap with international teams.
• Compensation and contractor terms are determined according to the applicable engagement agreement and discussed during the recruitment process.