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.

Lead Data Engineer

Key skills