Senior Data Platform Engineer for Large-Scale AI Data

Scale; Greater London; On-site; GBP 120,000 - 170,000; Full time

Job summary

Scale, based in London, is building data-driven AI platforms that power the most advanced models. The Platform Engineering team designs and develops core data storage, streaming, caching, and indexing systems and exposes them to product teams and customers.

You will drive the design, implementation, and reliability of these foundations, collaborate with cross-functional teams to deliver new features, and push improvements to development practices, tooling, and deployment using Kubernetes and

Qualifications

• 5+ years of full-time engineering experience with backend systems for large-scale data platforms.
• Experience with databases, streaming/processing, indexing, and caching technologies.
• Strong collaboration, mentoring, and ability to translate complex concepts to non-technical stakeholders.

Responsibilities

• Drive the design, implementation, and reliability of foundational data platforms and systems.
• Collaborate with cross-functional teams to define, design, and deliver new features.
• Proactively identify opportunities to improve development practices, processes, and tooling.
• Present technical information to teams and stakeholders, guiding development decisions.

Skills

Backend systems

Data storage

Streaming

Caching

Distributed systems

Cloud platforms

Education

Bachelor's degree in Computer Science or related field

Tools

MongoDB

Postgres

Kinesis

Flink

Spark

ElasticSearch

Redis

Trino

Presto

Snowflake

Kubernetes

Job description

Scale, based in London, is building data-driven AI platforms that power the most advanced models. The Platform Engineering team designs and develops core data storage, streaming, caching, and indexing systems and exposes them to product teams and customers.

You will drive the design, implementation, and reliability of these foundations, collaborate with cross-functional teams to deliver new features, and push improvements to development practices, tooling, and deployment using Kubernetes and