dunnhumby is the global leader in Customer Data Science, partnering with the worlds most ambitious retailers and brands to put the customer at the heart of every decision. We combine deep insight, advanced technology, and close collaboration to help our clients grow, innovate, and deliver measurable value for their customers. dunnhumby employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Nestlé, Unilever and Metro.

Experience : 5+ years of relevant experience.

WHY I DO MY JOB - Job Purpose :

Design, build, and operate scalable data science and analytics solutions that power business decision-making and digital products. This role bridges data science development, distributed data processing, and cloud platform engineering - ensuring that models, rules, and analytics workflows are performant, secure, observable, and production-ready on enterprise cloud platforms.

WHAT I DO - Key Accountabilities :

- Design and develop Spark based (PySpark) modules implementing custom data science logic, statistical methods, and complex business rules.

- Build reusable, scalable Python and SQL components for batch and near real-time analytics workloads.

- Optimize data pipelines for performance, reliability, and cost efficiency across large datasets.

- Translate analytical and data science requirements into production-grade, testable code.

Data Science Platform & Cloud Enablement :

- Contribute to and support the Applied Data Science hosting Spark workloads and web applications on GCP and/or Azure.

- Configure and manage cloud resources including :

i. Virtual Private Clouds (VPCs) and networking

ii. Secure access patterns and Role-Based Access Control (RBAC)

iii. IP whitelisting and external exposure of applications

- Enable observability through logging, monitoring, alerting, and traceability for both data and application workloads.

Infrastructure, CI/CD & Automation :

- Implement and maintain CI/CD pipelines for data science and Spark workloads.

- Use Docker for containerized execution and deployment.

- Apply Terraform / IaC principles to provision and manage cloud infrastructure.

- Ensure consistent, repeatable deployments across development, staging, and production environments.

Engineering Quality & Governance :

- Enforce best practices around code quality, testing, version control, and documentation.

- Partner with security and platform teams to ensure compliance with enterprise standards.

- Troubleshoot production issues across data pipelines, Spark jobs, and platform components.

Collaboration & Technical Leadership :

- Work closely with Data Scientists, ML Engineers, Platform Engineers, and Product teams to operationalize analytics solutions.

- Provide technical guidance and reviews for complex data science engineering work.

- Contribute to platform standards, reusable frameworks, and engineering roadmaps.

HOW I DO MY JOB - Qualifications :

Experience :

- 5-8 years of hands-on experience in data engineering, data science engineering, or analytics platform engineering.

- Proven experience delivering production systems built on Python, PySpark, BigQuery and SQL.

Core Technical Skills :

- Strong proficiency in :

i. Python (production-level code)

ii. PySpark / Apache Spark

iii. SQL across large analytical datasets

- Experience implementing complex analytical logic, business rules, or statistical workflows at scale.

Cloud & Platform Expertise :

- Hands-on experience with GCP and/or Azure, including :

i. Cloud consoles and managed services

ii. Networking concepts (VPCs, subnets, firewall rules)

iii. RBAC, IAM, and secure access controls

- Experience exposing internal applications securely to external consumers.

DevOps & Infrastructure :

- Practical knowledge of :

i. CI/CD pipelines

ii. Docker and containerized workloads

iii. Terraform / Infrastructure as Code

- Strong understanding of production reliability, monitoring, and observability.

Software & Systems Engineering :

- Solid grounding in software engineering principles, modular design, and testing strategies.

- Ability to debug and operate distributed systems in production.

Leadership & Mindset :

- Comfortable operating in a mixed role : hands-on development and platform enablement.

- Strong ownership mindset with the ability to work across data science, infra, and engineering boundaries.

- Clear communicator able to align technical solutions with business needs.

What you can expect from us :

We wont just meet your expectations. Well defy them. So youll enjoy the comprehensive rewards package youd expect from a leading technology company. But also, a degree of personal flexibility you might not expect. Plus, thoughtful perks, like flexible working hours and your birthday off.

Youll also benefit from an investment in cutting-edge technology that reflects our global ambition. But with a nimble, small-business feel that gives you the freedom to play, experiment and learn.

And we dont just talk about diversity and inclusion. We live it every day - with thriving networks including dh Gender Equality Network, dh Proud, dh Family, dh One, dh Enabled and dh Thrive as the living proof. We want everyone to have the opportunity to shine and perform at your best throughout our recruitment process. Please let us know how we can make this process work best for you.

Our approach to Flexible Working :

At dunnhumby, we value and respect difference and are committed to building an inclusive culture by creating an environment where you can balance a successful career with your commitments and interests outside of work.

We believe that you will do your best at work if you have a work / life balance. Some roles lend themselves to flexible options more than others, so if this is important to you please raise this with your recruiter, as we are open to discussing agile working opportunities during the hiring process.