Senior Engineering Manager - Data Platform Software engineering London

Checkout Ltd; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Checkout Ltd is looking for a Senior Engineering Manager to lead their Data and AI Platform Team in London. The role involves designing user-centric systems, mentoring a team of engineers, and ensuring compliance with regulatory controls. The ideal candidate has experience in leading data professionals in fast-paced environments, and proficiency with data processing systems and tools like Datadog, SQL, and AWS. This managerial position is critical for developing AI solutions suitable for various business needs.

Qualifications

• Experience leading and growing a team of data professionals.
• Experience as a data or software engineer, preferably in a fast-paced growing company.
• Ability to partner with Product Management to assess technical feasibility.

Responsibilities

• Lead the team in establishing technical architecture and designing user-centric systems.
• Manage a team of Junior and Senior Data Engineers and mentor them.
• Ensure compliance with regulatory controls and oversee AI use-cases.

Skills

Leadership of data professionals

Time management

Problem-solving

Experience with batch and stream data processing

Tools

Datadog

SQL

Terraform

Docker

AWS (ECS, Kinesis, SQS, CloudWatch)

GCP (BigQuery)

Job description

Senior Engineering Manager – Data & AI Platform Team

Checkout.com is looking for an ambitious Senior Engineering Manager to join our Data and AI Platform Team. Our team’s mission is to ultimately provide all the tools and platforms that enable data and AI to be easily leveraged for the benefit of our products, merchants, and internal collaborators and teams. We are focused on maximizing the time other teams spend solving business problems and innovating while minimizing the time spent on technical details around implementation, deployment and monitoring of their data and AI solutions. We build for scale; much of what we design and implement today will serve hundreds of teams and petabyte‑level volumes of data.

Key Responsibilities

• Lead the team in establishing and validating their understanding of technical architecture and designing end‑user centric systems and tools.
• Lead technical delivery and operational reliability by automating processes including monitoring and alerting, meeting SLAs/SLOs, and minimizing toil to ensure high‑performance, scalable engineering excellence.
• Manage a team of Junior and Senior Data Engineers while also being hands‑on when needed. Cultivate an environment where every team member feels included while mentoring them in career development and interpersonal skills.
• Oversee quarterly execution of roadmap delivery, ensuring the team focuses on the right priorities and efficiently manages dependencies and cross‑functional hurdles.
• Collaborate with Engineering and provide solutions to internal clients for data platform use cases.
• Build and improve strong relationships with engineers, managers, and product leads across multiple teams.
• Ensure compliance with regulatory controls from internal stakeholders such as InfoSec and Internal Audit as well as external auditors and regulators. Experience working in regulated industries is critical.
• Oversee AI use‑cases and lead the team to develop the infrastructure and tooling required to support AI governance and scalable GenAI production environments.

About You

• Experience leading and growing a team of data professionals.
• Experience as a data or software engineer, preferably in a fast‑paced growing company.
• Ability to partner with Product Management to assess technical feasibility, provide capacity estimates, and balance new features with architectural health.
• Excellent time management and proactive problem‑solving skills.
• Experience designing and implementing batch and stream data processing systems.
• Experience with Datadog, SQL, Terraform, Docker, AWS (ECS, Kinesis, SQS, CloudWatch), GCP (BigQuery).
• Knowledge of Airflow, Kafka, Flink, dbt, Kubernetes is highly beneficial.
• Good understanding of AI platforms, experience building infrastructure for LLM orchestration (e.g., vector databases, RAG pipelines, GPU scaling) is a significant plus.