Senior Data Engineer
As a Senior Data Engineer, you will be responsible for building and maintaining the core data foundations that power analytics, machine learning, and CRM activation across our US and UK markets. You will own the design and evolution of our data models and pipelines in platforms such as Snowflake, using tools like dbt and AWS to standardise schemas, improve data quality, and support advanced use cases including personalisation, experimentation, and automation. Your work will form the backbone for downstream capabilities such as propensity modelling, recommendation systems, and real‑time customer engagement. Working closely with Data Science and Marketing teams, you will translate analytical and product requirements into dependable data products. This is a senior, hands‑on role with a strong emphasis on engineering quality, documentation, and long‑term maintainability, helping ensure our data platform scales as our automation and personalisation ambitions grow.
Key Responsibilities
- Build and evolve shared data assets (e.g. feature tables or a feature store) that support consistent training, inference, and experimentation for data science models.
- Own the structure and quality of core datasets in Snowflake, including standardising schemas and ensuring data is accurate, well‑documented, and easy to use.
- Partner closely with Data Scientists and ML Engineers to translate modelling and serving requirements into dependable, scalable data products.
- Support CRM and activation use cases by enabling clean, timely data feeds to downstream platforms, including data models to facilitate BI development (Power BI).
- Directly manage and develop an Analytics Engineer.
Qualifications
- A proven track record as a data engineer building reliable data pipelines and datasets used by analytics, machine learning, or customer‑facing platforms.
- Degree in a technical or quantitative field (e.g. Computer Science, Engineering, Mathematics) or equivalent professional experience.
- Very strong SQL and data modelling skills.
- Strong experience with AWS and cloud computing, designing and operating data workloads using services such as S3, Lambda, IAM, CloudWatch, and other serverless or managed compute services.
- Experience building and maintaining data pipelines via APIs, integrating external systems and downstream platforms into the data stack.
- Experience developing transformation pipelines using tools like dbt, with solid engineering practices (testing, documentation, version control).
- Experience administering cloud data platforms, including managing roles and permissions, applying role‑based access control, and monitoring usage and cost across AWS and Snowflake.
- Comfortable collaborating with Data Scientists, ML Engineers, and CRM teams to translate requirements into dependable, scalable data products.
- Experience supporting customer or CRM data and feeding downstream activation platforms.
- Familiarity with orchestration tools (e.g. Airflow) and operating data pipelines at scale.
- Exposure to feature engineering concepts for machine learning.
We are a Disability Confident Committed Employer and actively encourage applications from individuals with disabilities and long‑term health conditions. If you require support during the interview process, please let us know.
#J-18808-Ljbffr…