Data Engineer

Data Engineer (AWS - Data Warehouse & Pipelines)

Full Time

Hybrid (3-4 Days in the office per week)

We’ve partnered with an amazing logistics company based in Reading, and we’re looking for a curious, hands‑on Data Engineer to support the maintenance and growth of their Data Warehouse and data pipelines. Reporting into the Senior Data Engineer, you’ll work closely with Data Analysts and the Finance team to integrate new data sources, model new processes, and deliver enhancements that expand functionality across the data platform.

This role suits someone who enjoys data modelling, solving technical problems, and wants to deepen their skills across dbt, SQL, and modern cloud data warehousing, while making a tangible impact on a fast-moving business.

As a Data Engineer, you will own key data engineering activities end-to-end, including:

• Monitor the data stack, proactively identifying and resolving issues (pipeline health, failed loads, data quality).
• Design, build, and modify data models using dbt Core (VS Code workflow).
• Collaborate with stakeholders to understand and model business processes in SQL/dbt.
• Review and assess new data sources, including feasibility, data quality, and integration approach.
• Integrate new sources into the data pipeline and data warehouse.
• Maintain strong version control discipline using Git (branching, PRs, reviews).
• Build and maintain lightweight Python utilities to retrieve data from APIs and support movement/loading of data.

• Strong SQL capability (preferably Postgre
  
  SQL or Snowflake SQL).
• Hands‑on experience with dbt (ideally dbt Core).
• Solid understanding of Cloud Data Warehouse concepts and design principles.
• Comfortable working with REST/SOAP APIs, and data formats such as JSON and YAML.
• Working knowledge of Git/version control.
• Basic Python for pulling data from APIs and supporting ETL/ELT tasks.

• Experience with Snowflake.
• Experience with Fivetran (or similar managed ingestion tooling).
• Familiarity with AWS S3 and AWS Lambda.
• Logical and structured problem‑solver.
• Comfortable working independently, but collaborative and proactive with colleagues.
• Strong attention to detail and pride in data quality.
• Positive mindset and a clear desire to learn and grow technically.
• Able to build effective relationships across the business (especially analytics and finance stakeholders).