What you’ll do:

• Design and maintain scalable ETL pipelines for data integration from platforms like YouTube, Google Ads, and Pinterest, ensuring seamless data ingestion and high-quality results.
• Optimize data syncing algorithms to handle large datasets efficiently, improving scalability and performance.
• Collaborate with AI researchers to transform machine learning models into production pipelines, delivering actionable insights in real time.
• Implement automated testing, monitoring, and validation processes to ensure data reliability and accuracy.
• Manage and optimize cloud infrastructure (e.g., AWS, GCP), focusing on cost efficiency and resource scalability.
• Build fault-tolerant systems to support high data volumes and ensure platform stability under heavy usage.
• Research and adopt emerging technologies to continuously improve data workflows and ML deployment.
• Troubleshoot and resolve technical challenges quickly and effectively.
• Work closely with product and engineering teams to align on technical goals and ensure seamless integration.
• Document best practices and mentor junior engineers, fostering knowledge sharing and team development.

What you’ll need to succeed:

• 4+ years of experience in data engineering roles with expertise in building and maintaining complex ETL pipelines.
• Strong programming skills in Python, with a deep understanding of system engineering and data infrastructure design.
• Experience deploying machine learning models in production environments and integrating them into scalable data pipelines.
• Proficiency with AI technologies such as PyTorch, TensorFlow, or Jax is a strong advantage.
• Solid knowledge of distributed systems, data modeling, and storage solutions for high-volume, real-time data.
• Familiarity with orchestration tools (Airflow, Temporal) and containerization (Docker, Kubernetes) for managing workflows.
• Proficiency with cloud platforms like AWS, GCP, Snowflake, or Databricks, including cost-effective resource management.
• Knowledge of ad-tech/mar-tech platforms and data integration from external APIs and large datasets.
• Strong problem-solving skills, with the ability to troubleshoot complex data issues across pipelines and integrations.
• Excellent collaboration and communication skills, with comfort working in cross-functional teams.