Data Platform Engineer for AI-Ready Pipelines

Empleora; Greater London; Hybrid; GBP 125,000 - 140,000; Full time

Benefits offered by this job

Competitive Equity

Enhanced parental leave

25 days holiday exc bank holidays

In-office lunches provided

Salary sacrifice pension and nursery

Hybrid London HQ

Annual team retreat

Job summary

Magnetic in London seeks a Data Platform Engineer to own the data foundation powering our AI agents. You’ll design ingestion pipelines, schemas and governance for procurement data spanning ERP, contracts, spreadsheets and emails, enabling autonomous agents to reason over it.

You’ll collaborate with AI, Platform and Product teams to steer data quality, cataloguing, and feature stores, while building end-to-end observability and secure data practices at scale.

Qualifications

• Experience designing and operating large-scale data pipelines and governance.
• Proven work with data warehouses/lakehouses and data catalogs.
• Ability to manage CI/CD and integrations across data platforms.

Responsibilities

• Design, implement and operate ingestion pipelines for high-volume data from global supply chain systems.
• Define and manage data schemas, catalogues, and feature stores from raw to analytics-ready state.
• Build end-to-end monitoring and observability for data pipelines, ensuring quality, latency and lineage.
• Champion secure, governed data practices across the platform.
• Collaborate with AI, Platform, and Product teams to provision data sets and contracts for analytics at scale.

Skills

Data quality

DataOps

Data at scale

Data warehouses

CI/CD

Stakeholder comms

Job description

Magnetic in London seeks a Data Platform Engineer to own the data foundation powering our AI agents. You’ll design ingestion pipelines, schemas and governance for procurement data spanning ERP, contracts, spreadsheets and emails, enabling autonomous agents to reason over it.

You’ll collaborate with AI, Platform and Product teams to steer data quality, cataloguing, and feature stores, while building end-to-end observability and secure data practices at scale.