Implement modern engineering standards, including CI/CD, testing, code reviews, and robust data quality/governance controls within the data team.
Ensure data quality, integrity, and governance through comprehensive testing, documentation, and monitoring processes.
Provide technical guidance and leadership for the design, development, and maintenance of robust, automated data pipelines and data management processes.
Design, build, and optimize scalable data solutions using Snowflake, Azure Data Services, and DBT for data transformation and modelling.
Optimize performance and cost of data workloads within Snowflake (query tuning, warehouse sizing) and Azure environments.
Manage and mentor a team of data engineers, supporting their professional development and career paths.
Collaborate with data architects, analysts, data scientists, and business stakeholders to understand data requirements and deliver high-impact solutions.
Plan work, estimate tasks, manage team's contribution to AGILE delivery via sprints and other key practices.
Work closely with cross-functional teams, including IT, finance, marketing, and operations, to understand their data needs and provide strategic guidance.
Collaborate with subject matter experts in these domains to ensure key requirements have been provided.
Contribute to the Key Performance Indicators (KPIs) related to data quality, analytics, and business impact.

Proven experience as a Data Engineer in a senior capacity, demonstrated line management or team leadership experience.
Strong hands-on experience with Snowflake (schema design, performance tuning, SnowSQL, RBAC).
Proven experience with DBT for modular data modelling, testing, documentation, and CI/CD integration.
Expertise in Azure Data Services (e.g., Azure Data Factory, Azure Data Lake, Azure Blob Storage) for end-to-end pipeline orchestration, AWS and GCP experience will be considered of nigh equal importance.
Strong proficiency in SQL and Python for data manipulation, automation, and complex data processing.
Experience with version control systems, such as GitHub, and CI/CD practices, such as Azure DevOps.
Familiarity with regulatory requirements, especially in the of data protection and privacy.
[Preferable, not essential] Strong understanding of the energy industry and / or regulated industries such as financial services or insurance.
[Preferable, not essential] Exposure to data visualisation software such as PowerBI, Tableau and Sigma
[Preferable, not essential] Familiarity to orchestration tools such as Airflow or Prefect.
[Preferable, not essential] Understanding of machine learning algorithms and statistical methods

Yu Energy

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