About the role
We are seeking a Senior Data Engineer (Data Engineer III) who is passionate about building modern data platforms and solving complex engineering challenges at scale. In this role, you will own data products end-to-end, from understanding business problems and designing scalable architectures to building production-grade pipelines that support analytics, AI, and operational excellence.
The candidate will have responsibilities across the following functions:
Data Product Development:
- Own data products from design through production.
- Design, build, and maintain scalable data pipelines and data products using Databricks, Spark, Python, SQL, and AWS.
- Develop robust batch and streaming pipelines aligned with modern Lakehouse architecture principles.
- Build reusable ETL/ELT frameworks, curated datasets, and self-service data products for analytics, AI/ML, and operational reporting.
- Optimise pipelines for performance, scalability, reliability, and cost efficiency.
- Implement incremental processing, CDC, and metadata-driven engineering frameworks.
Solution Design and Architecture:
- Partner with business stakeholders to understand requirements and translate them into scalable technical solutions.
- Design end-to-end data architectures, reusable frameworks, and high-performance data models.
- Evaluate architectural trade-offs and influence technical direction across the data platform.
- Drive solution design from concept through production deployment.
Engineering Excellence:
- Build production-grade solutions with a strong focus on quality, testing, observability, security, and reliability.
- Implement automated data validation, monitoring, lineage, and operational best practices.
- Write clean, maintainable code and contribute to reusable frameworks, CI/CD, DataOps practices, and code reviews.
- Troubleshoot production issues and continuously improve platform performance and developer experience.
Business Partnership:
- Collaborate with business stakeholders, product owners, analysts, architects, and engineers to solve high-impact business problems.
- Translate business requirements into scalable data products and trusted datasets.
- Communicate technical concepts effectively to both technical and non-technical audiences.
- Take end-to-end ownership of solutions from discovery and design to production support.
Requirements:
- 10+ years of experience in Data Engineering, with experience delivering enterprise-scale data platforms.
- Strong expertise in Databricks, Apache Spark (PySpark), Python, and SQL.
- Hands-on experience building cloud-native data platforms on AWS.
- Strong understanding of distributed data processing, Lakehouse architecture, ETL/ELT, and modern data engineering practices.
- Experience designing scalable data models and optimising large-scale data pipelines.
- Strong software engineering fundamentals, including Git, CI/CD, testing, and code quality.
- Excellent communication skills with the ability to influence stakeholders and lead technical discussions.