Data Platform Engineer – AI FInancial Intelligence

Sterlings are working partnered with an AI-driven financial intelligence company who are looking for a Data Platform Engineer with around 2–4 years’ commercial experience to help build and maintain the cloud-based data platform behind the company’s products.

The role combines Python development, AWS, data engineering, orchestration, DevOps and platform reliability.

You don’t need experience with every technology listed, but should have strong fundamentals and hands-on experience across several areas of modern data platform engineering.

Key Responsibilities:

• Build and maintain scalable data platforms and pipelines using Python and AWS.
• Develop reliable data ingestion, processing and transformation workflows.
• Work with orchestration tools such as Airflow.
• Build and maintain cloud infrastructure, APIs and containerised services.
• Develop CI/CD pipelines using GitHub Actions and Docker.
• Work with databases and data platforms including PostgreSQL, RDS, Redshift, Glue, Iceberg and Athena.
• Implement monitoring, logging and observability using CloudWatch, Grafana and Prometheus.
• Troubleshoot production issues and improve platform reliability, performance and scalability.
• Work closely with engineering and AI teams to integrate new data sources and platform capabilities.

Technical Experience

• 2–4 years’ commercial experience in data, software or platform engineering.
• Hands-on AWS experience.
• Experience with data pipelines or data platforms.
• Good understanding of CI/CD, DevOps and Git.
• Experience with Docker/other containerisation tools.
• Experience with databases, ideally PostgreSQL/RDS.
• Experience with workflow orchestration, ideally Airflow.

This is a hybrid role, with the person being in the office 3 days a week. (Vauxhall)

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