Databricks Data Engineer

£500 - £600 per day

Outside IR35

We're partnering with a leading online retail company that's transforming the way data and real-time intelligence shape customer experiences. Their mission is to harness cutting-edge data and streaming technologies to drive smarter decisions, improve efficiency, and create personalised journeys for millions of shoppers worldwide.

As a Senior Data Engineer, you'll play a key role in developing and optimising the backbone of the company's data platform. You'll be responsible for building and maintaining large-scale, real-time data pipelines that power analytics, machine learning, and operational systems across the business.

You'll collaborate with software engineers, data scientists, and analytics teams to ensure the platform delivers reliable, high-quality, and compliant data at scale. This is a hands-on engineering role that blends software craftsmanship with data architecture expertise.

Key responsibilities:

• Design and implement high-throughput data streaming solutions using Kafka, Flink, or Confluent.

• Build and maintain scalable backend systems in Python or Scala, following clean code and testing principles.

• Develop tools and frameworks for data governance, privacy, and quality monitoring, ensuring full compliance with data protection standards.

• Create resilient data workflows and automation within Airflow, Databricks, and other modern big data ecosystems.

• Implement and manage data observability and cataloguing tools (e.g., Monte Carlo, Atlan, DataHub) to enhance visibility and reliability.

• Partner with ML engineers, analysts, and analytics engineers to understand their data needs and enable advanced data use cases.

• Contribute to an engineering culture that values testing, peer reviews, and automation-first principles.

• Strong experience in streaming technologies such as Kafka, Flink, or Confluent.

• Advanced proficiency in Python or Scala, with a solid grasp of software engineering fundamentals.

• Proven ability to design, deploy, and scale production-grade data platforms and backend systems.

• Familiarity with data governance frameworks, privacy compliance, and automated data quality checks.

• Hands-on experience with big data tools (Airflow, Databricks) and data observability platforms.

• Collaborative mindset and experience working with cross-functional teams including ML and analytics specialists.

• Curiosity and enthusiasm for continuous learning - you stay up to date with the latest tools and trends in data engineering and love sharing knowledge with others.

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