Senior Data Engineer — Pipelines, Airflow & Databricks (Hybrid)

MARKS&SPENCER; Greater London; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Colleague discount

Holiday entitlement

Discretionary bonus

Pension scheme

Wellbeing support

Volunteer day

Job summary

Marks & Spencer is seeking a data engineering professional to design, build and maintain scalable data pipelines that support data science workflows. You will manage Airflow orchestration, work hands-on with Databricks, and contribute to engineering standards through GitHub collaboration.

Ideal candidates will have strong data engineering experience, the ability to own delivery, and a proactive, collaborative approach to cross-team work, including exposure to MLOps concepts.

Qualifications

• Strong data engineering experience with delivery accountability.
• Experience supporting data science workflows with data scientists.
• Clear, effective communicator across teams.
• Proactive problem solving with root-cause analysis.
• Adaptable, curious and eager to learn, with MLOps exposure.

Responsibilities

• Design, build and maintain robust data pipelines for data science use cases.
• Develop and manage Airflow orchestration for reliable pipelines.
• Hands-on work with Databricks to deliver data engineering solutions.
• Contribute to development via GitHub, code reviews and standards.
• Support repeatable, high-quality delivery in feature-led environments.

Skills

Data engineering

Airflow

Databricks

GitHub

MLOps exposure

Tools

Airflow

Databricks

GitHub

Job description

Marks & Spencer is seeking a data engineering professional to design, build and maintain scalable data pipelines that support data science workflows. You will manage Airflow orchestration, work hands-on with Databricks, and contribute to engineering standards through GitHub collaboration.

Ideal candidates will have strong data engineering experience, the ability to own delivery, and a proactive, collaborative approach to cross-team work, including exposure to MLOps concepts.