Hands-on senior data engineer to design and build scalable Lakehouse data solutions, develop ETL/ELT and streaming pipelines, tune Spark workloads, manage Delta Lake ingestion and governance, implement infrastructure with Terraform, enforce IAM and data security, monitor pipeline health, collaborate in Agile with data scientists and stakeholders, and explore AI tooling to enhance the platform.
We are working with a leading global Financial Services business to hire a Senior Data Engineer into their growing data engineering practice. This is a hands-on technical role sitting at the centre of a major enterprise data platform build, with scope to influence architecture, drive pipeline development, and contribute to AI initiatives across a complex, high-volume financial data environment.
Responsibilities:
- Design and develop scalable data solutions on a Lakehouse architecture platform, supporting enterprise-wide data processing and analytics
- Build, optimise, and maintain ETL/ELT pipelines and structured streaming workflows for both batch and real-time data ingestion
- Configure and tune clusters and Spark jobs to deliver consistent performance at scale
- Utilise Delta Live Tables and Unity Catalog to manage data ingestion, transformation, and access governance
- Apply IAM best practices and maintain compliance with data security standards across the platform
- Support infrastructure provisioning and resource management using Terraform
- Implement monitoring frameworks covering pipeline performance, data quality, and operational health
- Contribute to code reviews, technical documentation, and team knowledge-sharing
- Work within an Agile delivery model, collaborating closely with data scientists, analysts, and business stakeholders
- Explore emerging technologies and AI tooling to enhance development productivity and platform capability
Requirements
- 6+ years in data engineering, with at least 2 years hands-on experience with Databricks
- Strong Python and Spark programming skills
- Solid AWS experience across core services including S3, Glue, and Lambda
- Deep understanding of data modelling, SQL, and ETL/ELT design patterns
- Experience with Delta Lake, Lakehouse architecture, and Git-based version control
- Demonstrable use of AI tools within a professional development workflow
- Strong communication skills and the ability to work effectively across technical and non-technical teams
Desirable:
- Financial services or fund administration background
- Exposure to AI/ML implementation patterns and real-time data processing frameworks
- Multi-cloud experience beyond AWS
- API development or data governance framework experience
- Experience mentoring junior engineers
Benefits
Salary: to be discussed, depending on experience
Length: Permanent contract
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