Senior Data Engineer - #1677023

Data Integration Specialist / Performance Data Engineer

Location: London / Open to both Remote & Hybrid

Rate: £450 - £500 per day (Inside IR35)

Contract: 5 months (potential to extend)

The Opportunity:

We're partnering with a globally recognised business to find a Senior Data Engineer with a passion for data performance, cloud architecture, and scalable integration.

This role sits at the heart of operational performance, enabling data-driven decisions through robust data pipelines, smart modelling, and seamless integrations. You'll play a critical part in modernising and scaling the data estate across multiple cloud-native tools and platforms.

What You'll Be Doing:

• Build and optimise ETL/ELT pipelines across structured and unstructured data sources using Airbyte, Airflow, DBT Core, and AWS Glue
  


• Design and maintain dimensional models in Snowflake, including SCDs and best practices for indexing, clustering, and performance


• Collaborate cross-functionally with analysts and business teams to support Power BI and enterprise-wide self-serve analytics


• Implement best practices in data governance, including data quality checks, lineage tracking, and anomaly detection


• Automate data orchestration using tools such as Airflow, Lambda, or Step Functions
  


• Support financial and operational reporting through snapshot tables and audit-friendly data structures

What We're Looking For:

• Strong understanding of Enterprise Data Warehousing (EDW) with hands-on experience in Kimball-style modelling
  


• Expert-level SQL skills for complex transformation and query tuning


• Deep knowledge of Snowflake including optimisation, cost management, and architecture


• Experience with modern data stacks – especially DBT Core, Airbyte, and Airflow
  


• Familiarity with AWS data services (e.g., S3, Lambda, Step Functions)


• Proven ability to support scalable reporting frameworks and drive data reliability

Bonus Points For:

• Experience with data observability and CI/CD pipelines for data engineering


• Exposure to streaming platforms like Kafka or Kinesis


• Comfort working in fast-moving, cross-functional environments


• BI tool experience – Power BI, Tableau, or similar