Senior Data & ML Engineer - DataOps & AI Solutions

Croud; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Season Ticket loan

Work from home opportunities

Annual performance bonus

Gym Membership contributions

Share option scheme

Medical Insurance

Job summary

Croud is seeking a Senior Data and ML Engineer to join the Data Engineering team in the UK. You will deliver data-driven solutions across client portfolios and Croud’s products, mentoring juniors and shaping best practices.

Key focus areas include building production pipelines, ML/GenAI deployments (MLOps), and data governance, with a strong emphasis on cloud-based data warehousing and modern tooling. The role offers hybrid working options.

Qualifications

• Strong Python, SQL and database design skills.
• Hands-on experience building production data pipelines.
• Experience deploying ML/GenAI solutions (MLOps) and monitoring.
• Proficiency with dbt/Dataform or equivalent SQL tooling.
• Proactive, able to communicate with technical and non-technical stakeholders.

Responsibilities

• Support the Head of Data Engineering with delivering data solutions for clients and internal products.
• Own project implementation and mentor junior team members.
• Champion data and security best practices across projects.
• Design and implement data integrations, warehousing and automation tasks.
• Collaborate with clients to discuss progress and requirements.
• Develop and enforce DataOps governance policies and standards.
• Contribute to new business and cross-sell proposals with implementation details.

Skills

Python

SQL

Data pipelines

MLOps

GenAI

Communication

Team collaboration

Tools

dbt/Dataform

Docker

Terraform

CI/CD

BigQuery

GCP

Job description

Croud is seeking a Senior Data and ML Engineer to join the Data Engineering team in the UK. You will deliver data-driven solutions across client portfolios and Croud’s products, mentoring juniors and shaping best practices.

Key focus areas include building production pipelines, ML/GenAI deployments (MLOps), and data governance, with a strong emphasis on cloud-based data warehousing and modern tooling. The role offers hybrid working options.