Data Engineer/MLOps Engineer

View full description

Requirements:

• Strong knowledge of data system design; ability to break down problems and propose effective solutions.
• Proficiency in Python, with a strong understanding of software engineering best practices (testing, automation, code quality).
• Experience with containerisation and orchestration (Docker and Kubernetes).
• Infrastructure as Code (Terraform or similar).
• Experience with at least one distributed data-processing framework (Spark, Flink, Kafka, etc.).
• Familiarity with different storage solutions (e.g., OLTP, OLAP, NoSQL, object storage) and their trade-offs.
• Product mindset and ability to link technical decisions to business impact.
• Excellent cross-functional communication—able to partner with data scientists, software engineers, and product managers.

Responsibilities:

• Collaborate closely with product teams to implement robust, scalable data pipelines and ML workflows.
• Guide teams in adopting best practices around data engineering, infrastructure management, and MLOps.
• Surface practical insights from product teams to inform improvements in our internal Data Platform.
• Contribute actively to enhancing our data and ML infrastructure—focusing on usability, efficiency, reliability, and cost-effectiveness.
• Mentor and support engineers and data scientists in data engineering and MLOps best practices.