dunnhumby
Customer Data Science
DataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Engineer at dunnhumby. Skills: Data Engineering, MLOps, Python, Apache Spark, Cloud Platforms (GCP/Azure), Data Warehousing, Streaming Frameworks. designing, developing, and optimizing real-time and batch data pipelines. processing and analyzing data”
What You'll Achieve.
enable the accurate measurement of retail media campaigns across various channels; providing actionable insights; empowers stakeholders to optimize media investments, improve ROI, and enhance the overall customer experience; power our retail media measurement solutions; enabling near-real-time decision-making for critical business applications; driving actionable insights and measurable impact for the business
Industry & Context.
Excellent problem-solving skills; Ability to analyze complex data pipelines, identify performance bottlenecks, and suggest optimization strategies
What They're Looking For.
Must Have
3+ years of expertise in data engineering, proven track record of designing and optimizing scalable solutions, experience with cloud data warehouses and query engines, experience with data cataloging, metadata management, and lineage tools, programming skills in Python, experience in frameworks like FastAPI or similar API frameworks, proficiency in unit testing and ensuring code quality, hands-on experience with version control tools like Git, Ability to analyze complex data pipelines, identify performance bottlenecks, and suggest optimization strategies, Work collaboratively with infrastructure teams to ensure a robust and scalable platform for data science workflows, Excellent problem-solving skills, ability to work effectively in a team environment, Proven mentoring and communication skills, fostering collaboration across teams, effectively sharing technical expertise
Nice to Have
Experience with data quality tooling, hands-on experience with cloud-based data stores like Redshift or BigQuery, Experience with microservices architecture, containerization using Docker, orchestration tools like Kubernetes, Working knowledge of machine learning workflows with feature engineering, model training, deployment, and monitoring etc., Understanding of logging, monitoring, and alerting for production-grade big data pipelines, Good working knowledge with NoSQL databases such as MongoDB, Cassandra, or DynamoDB
What You'll Do.
and optimizing real-time and batch data pipelines
processing and analyzing data
contribute to MLOps practices
building scalable infrastructure to support machine learning workflows
automating model deployment
monitoring performance
ensuring reproducibility across environments
bridging the gap between data engineering and machine learning
enabling seamless integration of predictive models into production pipelines
designing scalable pipelines
championing best practices in data engineering and MLOps
ensure the reliability
and performance of our data and ML infrastructure
How You'll Work.
Team & Collaboration
collaborate closely with Data Scientists, Analysts, Lead Engineers, and Product Managers; Work collaboratively with infrastructure teams; fostering collaboration across teams
Communication Scope
Proven mentoring and communication skills; effectively sharing technical expertise
Full Job Description
dunnhumby is the global leader in Customer Data Science, partnering with the world’s 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. Retail Media is transforming how advertisers connect with consumers through personalized and targeted campaigns across retailers' digital and physical touchpoints. Retail Media Measurement plays a pivotal role in ensuring the effectiveness of these campaigns, driving value for advertisers, retailers, and consumers alike. This role focuses on designing, building, and scaling solutions that enable the accurate measurement of retail media campaigns across various channels. By providing actionable insights, it empowers stakeholders to optimize media investments, improve ROI, and enhance the overall customer experience. Job Title: Data Engineer Job Summary We are seeking a talented and self-driven Senior Data Engineer to design, develop, and optimize real-time and batch data pipelines that power our retail media measurement solutions. In this role, you will work with Python, Apache Spark, and modern streaming frameworks to process and analyze data, enabling near-real-time decision-making for critical business applications in the retail media space. Beyond traditional data engineering, you will also contribute to MLOps practices—building scalable infrastructure to support machine learning workflows, automating model deployment, monitoring performance, and ensuring reproducibility across environments. Your work will help bridge the gap between data engineering and machine learning, enabling seamless integration of predictive models into production pipelines.
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