Data Engineer – People Analytics

About the role

Arm is looking for a Data Engineer to build and scale data foundations that enable analytics, workflow automation, and AI‑driven decision‑making for the People function. You will work at the intersection of data engineering, AI, and business transformation, delivering trusted data pipelines and intelligent systems that improve workforce insights and employee experience.

Key responsibilities

• Design, build, test and maintain reliable data pipelines, APIs and integrations across multiple systems.
• Develop scalable transformation workflows, reusable data products and curated datasets for analytics, reporting and AI use cases.
• Improve data quality, observability, lineage and operational monitoring across data workflows.
• Implement workflow automations that increase operational efficiency and scalability.
• Collaborate with stakeholders to translate business and technical requirements into practical data and automation solutions.
• Support secure, responsible and privacy‑conscious handling of workforce and organisational data.
• Contribute to data modelling, governance and engineering best practices on the data platform.
• Work closely with analytics, AI‑focused teams and Data Operations to enable trusted, accessible data products and intelligent workflows.

Required profile

• Experience in data engineering, software engineering or platform engineering.
• Strong SQL capability and practical experience with Python or a similar language.
• Hands‑on experience building and maintaining ETL/ELT pipelines in production.
• Understanding of data modelling, transformation logic and data‑quality practices.
• Experience with cloud data platforms, modern data warehouses or distributed processing environments.
• Strong problem‑solving, communication and collaboration skills.
• Curiosity, learning agility and a genuine interest in solving meaningful problems through technology.

Required skills

• SQL
• Python
• ETL/ELT pipeline development
• Data modelling
• Cloud data platforms
• Modern data warehouses
• Distributed processing environments
• Databricks
• APIs and data pipeline integration
• Workflow automation