[Hiring] Senior Data Engineer @Welo Global

Role Description

The Senior AI/ML Data Engineer is a hands-on technical contributor responsible for designing, building, and maintaining the data infrastructure that powers AI/ML product features across the organization. This includes:

• Building ETL pipelines between diverse data sources and destinations.
• Architecting new solutions to support production-level APIs.
• Syncing application data into analytics-ready warehouses.
• Tackling ad hoc data challenges that don't fit neatly into existing patterns.

The ideal candidate thrives in a fast-moving product engineering environment, can navigate ambiguity in requirements, take ownership of problems from investigation through production deployment, and maintain a high standard of documentation and data quality.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4-6 years of experience in data engineering or related fields, with a focus on AI/ML.
• Strong Python skills, including experience with migration frameworks (e.g., Alembic, SQLAlchemy).
• Advanced SQL across both transactional databases (PostgreSQL or similar) and columnar data warehouses (AWS Redshift strongly preferred).
• Proficiency in cloud services (e.g., dbt, AWS, Azure, GCP).
• Proficiency with AWS services: Redshift, S3, Lambda, IAM.
• Strong Git and GitHub skills, including branching strategies, pull request workflows, and code review practices.
• Expertise in building scalable, fault-tolerant data pipelines.
• Excellent problem-solving and analytical skills, with the ability to identify and address performance bottlenecks.
• Experience mentoring junior engineers or leading small teams, with strong technical leadership capabilities.
• Excellent communication and teamwork skills, with the ability to work effectively in cross-functional teams.

Requirements

• Design, build, and maintain ETL pipelines that move data between diverse sources and destinations.
• Partner with product managers, research scientists, QA, and platform engineers to translate business and ML requirements into data engineering work.
• Investigate and resolve data integrity issues surfaced by stakeholders.
• Ensure data security, quality, and compliance standards are met across all data pipelines and processes.
• Automate the end-to-end process of data ingestion, transformation, and processing.
• Develop and maintain complex datasets while ensuring a uniform design and engineering methodology.
• Mentor junior engineers, conduct peer reviews, and identify opportunities to improve team processes.

Benefits

• Opportunity to work in a fast-paced, innovative environment.
• Access to cutting-edge technology and tools.
• Professional development and mentorship opportunities.
• Collaborative and inclusive company culture.