Senior Data Engineer - Lakehouse Pipelines & AI
A hands-on Senior Data Engineer building batch and real-time ETL/ELT pipelines on a Databricks Lakehouse platform for a global financial services client in Dublin. Core stack: Python, Spark, AWS (S3, Glue, Lambda), Delta Lake/Unity Catalog, Terraform, with involvement in AI initiatives.
- Compensation
- Not disclosed
- Location
- Dublin, Ireland
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Contract
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 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 ability to work across technical and non-technical teams
Nice to have
- 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
Full description
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