Design and maintain scalable pipelines for visual and voice data, including ingestion, processing, validation, testing, observability, and automation. Build cloud, on-premise, and containerized data infrastructure; optimize storage, retrieval, and caching for large media assets; debug complex data issues; and collaborate with data scientists, operators, and product teams to support machine learning workflows.
As a Data Engineer, you’ll architect and maintain the pipelines that power our products and services. You’ll 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 you’re 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).
About You
- Innovative
- Like challenges
- Adaptable
- Calm under pressure
- Strong communication abilities
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