Data Engineer

Eurobase People; Greater London; On-site; GBP 65,000 - 90,000; Full time

Job summary

Eurobase People seeks engineers who can design and build scalable data pipelines and cloud-based platforms to support analytics, ML, and AI applications.

The role focuses on delivering reliable data infrastructure and ownership from design to production, tackling complex engineering problems and improving data reliability across teams.

Qualifications

• Proficient in Python and SQL for building scalable ETL/ELT pipelines.
• Experience designing data platforms in cloud environments (Databricks, Snowflake).
• Strong understanding of data modeling, warehousing and pipeline architecture.

Responsibilities

• Design, build and maintain scalable ETL/ELT pipelines using Python and SQL.
• Develop and optimise data processing workflows across cloud environments.
• Build and maintain data platforms with Databricks, Snowflake and cloud-native services.
• Work with large datasets using Spark and distributed processing frameworks.
• Implement data quality checks, testing, monitoring and observability.
• Collaborate with analytics, software engineering, product and ML teams.
• Hands-on production-data-pipeline delivery experience.
• Experience with AWS, Azure or GCP.

Skills

Python

SQL

Data modeling

ETL/ELT architecture

Spark

Git

Tools

Databricks

Snowflake

AWS

Azure

GCP

Airflow

Job description

The focus is on engineers who can build scalable data pipelines, develop cloud-based data platforms and deliver reliable data infrastructure to support analytics, machine learning and AI-driven applications.

I'm particularly interested in professionals who enjoy solving complex engineering problems, improving data reliability and taking ownership of technical solutions from design through to production.

Key responsibilities

• Design, build and maintain scalable ETL/ELT pipelines using Python and SQL.
• Develop and optimise data processing workflows across cloud-based environments.
• Build and maintain data platforms using technologies such as Databricks, Snowflake and cloud-native services.
• Work with large datasets using Apache Spark and distributed processing frameworks.
• Implement data quality checks, testing, monitoring and pipeline observability.
• Collaborate with analytics, software engineering, product and machine learning teams.
• Strong commercial experience with Python and SQL.
• Experience building and maintaining production data pipelines.
• Hands-on experience with AWS, Azure or GCP.
• Understanding of data modelling, warehousing and ETL/ELT architecture.
• Experience troubleshooting data quality, performance and reliability issues.
• Familiarity with Git, testing and software engineering best practices.