Prototyping analytical tools developed by the engineering and analytics teams for Clir Renewables. Collaborating on automation scripts and methods for renewable energy analysts.
About the role
- Staff Analytics Engineer supporting data enablement and operational reporting for Corporate teams at GoodLeap. Collaborating with stakeholders and developing datasets and dashboards for analytics solutions.
Responsibilities
- Engage with stakeholders and business teams to understand priorities and identify opportunities to advance their analytics solutions
- Provide support to stakeholders by triaging requests, investigating data issues, and building tools and processes to support data needs.
- Develop datasets and dashboards within an agile framework.
- Share new data tools with non-technical stakeholders and educate end users on how best to access the data they need.
- Assist business users in identifying and deploying the right tool for each use case, including performing new tool and technology evaluations.
- Work with other members of the Analytics team to define, implement, and test new data sources and pipelines and ensure that new reports are built on consistent, reliable, and maintainable data foundations.
- Provide input to Data Engineering on requirements and enhancements to existing data ingestions, pipelines, and platforms.
Requirements
- BS in Computer Science, DS, or related technical degree or equivalent work experience
- 6-10 years' experience as an Analytics Engineer, BI Analyst, Data Analyst, or similar role
- 5+ years' hands-on SQL experience in a work environment
- Experience with data management, documenting process and data flows, and ensuring data quality
- Experience with ETL processes, software development, and lifecycle awareness
- Strong communication skills, including the ability to clearly describe technical work to non-technical stakeholders
- Demonstrated ability to quickly learn new technologies and think critically about their strengths and weaknesses
- Analytical mindset and willingness to identify and question assumptions
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