MLOps / Data Engineer

Role Overview

MLOps / Data Engineer bridging data science and production systems to ensure ML models work reliably in real-world environments. You’ll build the pipelines and infrastructure that turn listener intelligence into structured, queryable signals for advertising buyers.

Responsibilities

• Design and maintain CI/CD pipelines for ML workflows using tools such as GitHub Actions, Azure DevOps, or Jenkins.
• Build and optimize large-scale data processing pipelines in Apache Spark using PySpark and Scala.
• Deploy and manage Databricks environments, including cluster efficiency, job scheduling, and cost optimization.
• Productionize ML models with data scientists by integrating into APIs or batch processing that feeds machine-readable audience signals.
• Implement automated testing, monitoring, and alerting to ensure ML pipeline reliability and reproducibility.
• Apply best practices for version control, model registry management, and environment reproducibility.
• Help evolve listener data infrastructure toward agent-compatible supply (live, structured, queryable feeds).

Requirements

• Proven experience in Data Engineering, MLOps, and DevOps, focused on automation and scalability.
• Strong Python skills and hands-on experience with Apache Spark.
• Advanced Databricks expertise, including Delta Lake and structured streaming (feature engineering).
• Solid understanding of CI/CD principles and tools (e.g., GitHub Actions, Jenkins, Azure DevOps, GitLab CI, ArgoCD).
• Familiarity with AWS, Azure, or GCP for data/ML workloads.
• Ability to collaborate across cross-functional teams and a strong architectural mindset for trade-offs (cost, performance, scalability, maintainability).

Nice to Have

• Scala is a huge plus.