As a Data Engineer, youll architect and maintain the pipelines that power our products and services.

Youll work at the intersection of ML, media processing, and infrastructure; owning the data tooling and automation layer that enables scalable, high-quality training and inference.

If youre a developer who loves solving tough problems and building efficient systems, we want you on our team.

Key Responsibilities :

- Design and maintain scalable pipelines for ingesting, processing, and validating datasets with main focus visual and voice data.

- Work with other teams to identify workflow optimisation potential, design and develop automation tools, using AI-driven tools and custom model integrations and scripts.

- Write and maintain tests for pipeline reliability.

- Build and maintain observability tooling in collaboration with other engineers to track data pipeline health and system performance.

- Collaborate with data scientists, operators, and product teams to deliver data solutions.

- Debug and resolve complex data issues to ensure system performance.

- Optimise storage, retrieval, and caching strategies for large media assets across environments.

- Deploy scalable data infrastructure using cloud platforms as well as on-premise and containerization.

- Deepen your knowledge of machine learning workflows to support AI projects.

- Stay current with industry trends and integrate modern tools into our stack.

Must Haves :

- 3+ years in data engineering or related backend/infrastructure role.

- Strong programming skills in Python or similar languages.

- Experience with software development lifecycle (SDLC) and CI/CD pipelines.

- Proven experience building and testing data pipelines in production.

- Proficiency in Linux.

- Solid SQL knowledge.

- Experience with Docker or other containerisation technologies.

- Proactive approach to solving complex technical challenges.

- Passion for system optimisation and continuous learning.

- Ability to adapt solutions for multimedia data workflows.

Nice to Have :

- Experience with Kubernetes (k8s).

- Knowledge of machine learning or AI concepts.

- Familiarity with ETL tools or big data frameworks.

- Familiarity with cloud platforms (e.g., AWS, GCP, Azure).