Design, build, and maintain scalable data pipelines in modern lakehouse architectures.
Develop clean, efficient, and production-ready Python and SQL code.
Implement ETL/ELT processes, transformations, and orchestration workflows.
Model data using medallion architecture (Bronze/Silver/Gold), star schemas, and SCDs.
Integrate multiple data sources (APIs, databases, SaaS platforms, flat files).
Deploy pipelines using CI/CD tools and version control best practices.
Leverage AI tools (e.g., agent-based workflows, automation scripts) to improve delivery speed and quality.
Collaborate directly with clients on requirements, architecture, and delivery updates.
Monitor, troubleshoot, and optimise pipelines for performance and reliability.
Ensure data quality, integrity, and production readiness.

5+ years of professional experience in data engineering.
Must have experience with Databricks or Fabric.
Strong Python skills for production environments.
Advanced SQL (CTEs, window functions, performance tuning, complex joins).
Hands-on experience with modern data platforms (lakehouse, pipelines, distributed processing).
Experience with dimensional modelling and analytics-ready data design.
Solid understanding of CI/CD, Git workflows, and deployment practices.
Strong communication skills with the ability to work directly with clients.
Ability to translate business requirements into technical solutions.
AI Proficiency (Required): This role requires strong, practical experience using AI in development workflows.
Comfortable working in modern development environments with AI-assisted tooling.
Proven experience building AI-powered tools, automations, or agents with real business impact.
Ability to use AI across the development lifecycle (design, coding, debugging, testing, documentation).
Strong judgment in validating and refining AI-generated outputs.
Experience with prompt design, context handling, and tool integrations.

Senior Data Engineer

Key skills