Senior Data Engineer

Key Responsibilities

• Develop and maintain Snowflake-based data products and data models.
• Build and manage ELT pipelines using dbt and modern data engineering practices.
• Implement automated data quality checks, monitoring and observability across data pipelines.
• Deliver reliable, secure and well-governed datasets for analytics and operational use cases.
• Collaborate with Product Managers, Analysts and Business Stakeholders to understand data requirements and deliver fit-for-purpose solutions.
• Design solutions with scalability, maintainability, performance and cost optimisation in mind.
• Support real-time and near-real-time data integration patterns using Kafka and event-driven architectures.
• Develop reusable frameworks, standards and engineering best practices across the data platform.
• Implement CI/CD pipelines and automated deployment processes.
• Troubleshoot and resolve data quality, performance and reliability issues.
• Contribute to platform modernisation and continuous improvement initiatives.
• Work closely with Data Governance teams to ensure compliance with GDPR, security and data management standards.
• Participate in code reviews and promote engineering excellence across the team.
• Mentor junior engineers and contribute to knowledge sharing across the Data Analytics community.
• Evaluate emerging technologies and recommend improvements to the platform and engineering practices.
• Support AI and advanced analytics initiatives through the delivery of trusted, high-quality data assets

Knowledge, skills and experience required

Mandatory Technical Skills

Data Engineering

• Advanced SQL
• Python
• Data Modelling (Dimensional Modelling, Kimball, Data Vault fundamentals)
• ELT/ETL design and development
• Data Warehousing concepts
• Data Lakehouse concepts
• Data Quality Engineering

• Snowflake
• dbt
• Git
• CI/CD pipelines
• Data Observability and Monitoring

• AWS fundamentals
• Infrastructure as Code (Terraform)
• APIs and data integrations
• Containerisation fundamentals (Docker)

• Kafka fundamentals
• Event-Driven Architecture concepts
• CDC (Change Data Capture) concepts

• Agile delivery (Scrum/Kanban)
• Test-driven engineering principles
• Code review practices
• Performance optimisation
• Cost optimisation

Preferred Skills

• Confluent Kafka Platform
• Airflow
• Databricks
• PySpark
• Data Mesh concepts
• Data Products and Product Operating Model
• Data Governance and Lineage tools
• Monitoring and Observability platforms
• Kubernetes fundamentals

Nice to Have

• Generative AI fundamentals
• LLM integration patterns
• RAG architectures
• Vector databases
• AI Agent frameworks
• Machine Learning platform awareness
• Real-time analytics architectures

Person Specification

• Self-motivator with a desire to learn new skills and embrace new technologies in a constantly changing technology landscape
• Strong problem-solving and analytical skills.
• Passion for building scalable and maintainable data solutions.
• Self-motivated with a continuous learning mindset.
• Strong stakeholder management and communication skills.
• Ability to communicate technical concepts to non-technical audiences.
• Collaborative team player who thrives in agile environments.
• Quality-focused with strong attention to detail.
• Ability to balance business priorities with technical excellence.
• Proactive in identifying opportunities for improvement and innovation.
• Comfortable working across global teams and multiple business domains.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.