Yu Energy

Data Analyst

• Uphold and deliver data strategy aligned with business goals and industry best practices.
• Enforce data governance policies and procedures to ensure data quality, integrity, and security.
• Ensure compliance with data protection regulations and standards, such as GDPR, and implement measures to safeguard customer and company data.
• Front end engineering using Python, SQL, DAX and M Language from established data warehouse solutions.
• Adopting and practising new languages and practices to enhance data capabilities within the organisation.
• Drive the development of advanced analytics capabilities to extract valuable insights from large datasets.
• Collaborate with business stakeholders to define and deliver actionable reports and dashboards.
• Create and enhance data visualizations in tools such as PowerBI and Tableau
• Collaborate with business stakeholders to ensure the visualisations are providing insight to action.
• Work closely with cross-functional teams, including IT, finance, marketing, and operations, to understand their data needs and provide strategic guidance.
• Document and advise departmental analysts on best actions and process improvement.
• Contribute to the Key Performance Indicators (KPIs) related to data quality, analytics, and business impact.

Data Engineer

• Uphold and deliver data strategy aligned with business goals and industry best practices.
• Enforce data governance policies and procedures to ensure data quality, integrity, and security.
• Ensure data compliance and security needs are met in system construction and access control.
• Apply security measures such as RLS and Column Masking to keep the data secure to the correct parties.
• Design, build, and optimise scalable data solutions using Snowflake, Azure Data Services, and DBT for data transformation and modelling.
• Support the Head of Data in managing the project prioritisation, identifying any potential threats to delivery.
• Optimise and validate performance and cost of data workloads within Snowflake (query tuning) and Azure environments.
• Use programming language to maintain data sets, databases, tables, data lakes or data warehouses.
• Implement big data tools such as Apache Spark / Kafka to automate data pipelines.
• Develop connections between multiple sources of data with APIs or database connectors.
• Work closely with cross-functional teams, including IT, finance, marketing, and operations, to understand their data needs and provide strategic guidance.
• Collaborate with the Senior Data Engineer, Data Engineers and Data Analysts to determine design needs.
• Contribute to the Key Performance Indicators (KPIs) related to data quality, analytics, and business impact.