Data Engineer

JOB OVERVIEW

The mission of the Data Analytics Engineer at Warwick Energy is to bridge the gap between data engineering and business intelligence (BI) teams seamlessly. In this role, you will convert raw data into well-structured, accessible datasets and design scalable data models that enable meaningful analytics and reporting.

A critical part of this role involves applying data engineering principles to build and maintain trusted, high-quality data objects within the data warehouse. By ensuring the accuracy, consistency, and reliability of these data assets, you will create a strong foundation for scalable analytics and BI solutions. Your work will enable the organization to leverage a single source of truth for decision-making, fostering confidence in data-driven insights.

As a detail-oriented professional, you will ensure high levels of accuracy and thoroughness in data preparation and documentation. Your problem-solving skills will be essential as you apply analytical thinking to tackle complex challenges in data workflows and in shaping the data.

Collaboration is key in this role, as you will communicate effectively across teams and foster a cooperative working environment. An eagerness for continuous improvement will drive you to learn and implement efficient data solutions and creating robust data products that power BI applications, we encourage you to join our team.

KEY JOB RESPONSIBILITIES

• Data Transformation: Bridge data engineering and BI teams to convert raw data into structured, actionable datasets.

• Data Modeling: Design scalable data models and semantic layers to optimize BI tool analysis.

• Strengthen Data Infrastructure: Collaborate with data engineers to design and maintain robust data models for BI projects.

• Workflow Development: Develop and maintain version-controlled analytics workflows using tools like DBT.

• Business Alignment: Ensure analytical outputs meet business objectives and are accessible to all users.

• Technical Documentation: Create clear documentation for data models, workflows, and analytics solutions for both technical and non-technical stakeholders.

• Collaboration: Work with cross-functional teams to support data-driven decision-making and promote best practices in data analytics.