Act as Chapter Lead for Data Engineering, providing technical leadership, mentoring, and guidance across the data engineering community
Set and uphold engineering standards, best practices, and ways of working
Take ownership and accountability for the quality, maintainability, and scalability of data engineering solutions
Design, develop, modernise, and test data engineering pipelines, including ETL/ELT processes, reports, and data visualisations
Build and maintain robust real-time and batch data pipelines to meet evolving client and business needs
Load and transform customer data into target platforms using scalable and reusable patterns
Implement data management and integration solutions using APIs
Write complex, performant T-SQL queries and high-quality Python code
Ensure solutions are well-documented, reusable, maintainable, and production-ready
Apply source control best practices using Git and contribute to CI/CD pipelines
Elicit, analyse, and refine multi-stakeholder requirements, translating business needs into technical solutions
Act as a client-facing consultant, confidently engaging with technical and non-technical stakeholders
Collaborate closely with internal teams and client stakeholders to provide technical leadership and delivery assurance
Support pre-sales and sales activities where required, including solution shaping and technical input

Strong experience designing, building, testing, and deploying data pipelines on the Azure platform
Advanced T-SQL and Python development skills
Proven experience with Git and working within source-controlled, CI/CD-driven environments
Expertise in Data analytics platforms (such as Hadoop, Data Bricks or Azure Synapse), Columnar data formats (e.g., Parquet, ORC, Arrow, etc), on-prem to cloud migrations, and both SQL and NoSQL data stores
Strong knowledge of batch and real-time data pipeline architectures, including event streaming
Experience in data deduplication, transformation, and conformance from source to target structures
Solid understanding of logical and physical data modelling and storage design
Ability to address observability, data governance, privacy, and architectural concerns
Comfortable working autonomously while adopting new tools and technologies quickly

Principal Data Engineer

Key skills