At a Glance
- Tasks: Design and build scalable data pipelines using AWS, Databricks, and PySpark.
- Company: Join a high-impact engineering team in a collaborative environment.
- Benefits: Competitive salary of £80k-£95k, hybrid work model, and career growth opportunities.
- Other info: Start in August and enjoy a dynamic, forward-thinking workplace.
- Why this job: Work with cutting-edge technology and make a real impact on marketing insights.
- Qualifications: Experience in cloud environments, AWS services, and strong skills in PySpark and SQL.
The predicted salary is between 60000 - 80000 £ per year.
Location: This is a hybrid engagement represented by 2 days/week onsite, either in Central London or Glasgow.
Start Date: Must be able to start mid-August.
Salary: £80k-£90k (Senior) | £90k-£95k (Lead)
About The Role
Our partner is looking for a Senior Data Engineer to join a high-impact engineering team delivering scalable data solutions for complex marketing and customer insight use cases. This is an opportunity to work on cutting-edge data pipelines, cloud-native platforms and real-time data flows in a collaborative, forward-thinking environment.
You’ll be involved in designing and building production-grade ETL pipelines, driving DevOps practices across data systems and contributing to high-availability architectures using tools like Databricks, Spark and Airflow - all within a modern AWS ecosystem.
Responsibilities
- Architect and build scalable, secure data pipelines using AWS, Databricks and PySpark.
- Design and implement robust ETL/ELT solutions for both structured and unstructured data.
- Automate workflows and orchestrate jobs using Airflow and GitHub Actions.
- Integrate data with third-party APIs to support real-time marketing insights.
- Collaborate closely with cross-functional teams including Data Science, Software Engineering and Product.
- Champion best practices in data governance, observability and compliance.
- Contribute to CI/CD pipeline development and infrastructure automation (Terraform, AWS DevOps).
- Provide input into technical decisions, peer reviews and solution design.
Requirements
- Proven experience as a Data Engineer in cloud-first environments.
- Strong commercial knowledge of AWS services (e.g. S3, Glue, Redshift).
- Advanced PySpark and Databricks experience (Delta Lake, Unity Catalog, Databricks Jobs etc).
- Proficient in SQL (T-SQL/SparkSQL) and Python for data transformation and scripting.
- Hands-on experience with workflow orchestration tools such as Airflow.
- Strong version control and DevOps exposure (Git, GitHub Actions, Terraform).
- Familiar with data quality tools and metadata/cataloguing (e.g. Great Expectations, Unity Catalog).
- Beneficial: MarTech domain knowledge.
