We need an English-fluent Jr. Data Engineer, based in Latin America, available to work remotely.

We are looking for someone with exceptional communication and relationship-building skills, who embraces changes while maintaining strong attention to detail. An interested and proactive person, who’s constantly learning and improving their skills.

Are you the one we are looking for?

Responsibilities:

• Enhance and maintain the infrastructure powering our analytics and data products.
• Ingest new data from files, APIs, and external databases into a centralized data store, ensuring seamless integration and data flow.
• Continuously monitor, optimize, and troubleshoot data pipelines and infrastructure for performance, scalability, and cost-effectiveness.
• Work closely with business stakeholders, including Analysts and Data Scientists, to understand data needs and translate business requirements into technical solutions.
• Implement and enforce data governance policies, ensuring adherence to best practices in data security, integrity, privacy, and regulatory compliance.
• Contribute to and drive efforts to ensure high data quality across the organization by identifying and resolving issues in data pipelines and workflows.
• Participate in technical architecture discussions, offering expertise in designing scalable, efficient data systems. Engage in peer code reviews to maintain high standards of code quality and best practices.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent work experience.
• 1+ years working as a Data Engineer or in a similar technical role with a focus on building and maintaining data infrastructure and pipelines.
• Experience with AWS (Redshift, RDS) and Google Cloud (BigQuery, Firebase) for managing and working with cloud-based databases and analytics tools.
• Proficiency in at least one programming language (Python, Java, Scala, etc.) to automate data pipeline workflows.
• Hands-on experience with real-time data streaming solutions (AWS Kinesis) to handle large-scale, real-time data ingestion.
• Familiarity with data pipeline orchestration tools such as dbt and Airflow.
• Strong knowledge of data warehousing concepts and ETL (Extract, Transform, Load) best practices.