Sr. Enterprise Data Analyst
Posted 1 day ago by RKube Inc
Negotiable
Undetermined
Remote
Remote
Summary: The Sr. Enterprise Data Analyst is responsible for driving the completion of complex data projects and solutions while ensuring adherence to best practices in data architecture and analysis. This role involves close collaboration with business stakeholders to address their data needs and develop effective data models and pipelines. The analyst will also serve as a subject matter expert in various data domains and work to enhance data quality and accessibility for the organization.
Key Responsibilities:
- Drive completion of data projects and deliverables including the execution of highly complex data solutions while demonstrating best practices, processes and standards for user story development, data analysis, architecture, Modeling, design, coding, and testing.
- Lead or assist in the development, documentation, and maintenance of the enterprise data architecture and its deliverables including the design principles, models, and mappings for transactional and analytical systems serving both strategic and tactical needs.
- Work closely with the business, mapping their most pressing business problems to data and analytic solutions.
- Develop data models and data transformation logic.
- Work with data engineers to implement data pipelines.
- Prototype data solutions to enable the measurement or monitoring of key performance indicators.
- Collaborate with senior management to understand data needs in support of departmental goals.
- Partner with product owners and managers in developing scope, dependencies, estimates, project plans, schedules, status reporting, and issue/risk management.
- Become the subject matter expert in multiple data domains.
- Responsible for defining company standards around data quality.
- Maximize opportunities to implement self-service to allow the business to find the data they need quickly, easily, and accurately.
- Enable data science team to build ML models by designing and providing high quality data.
- Establish and maintain effective and positive relationships with internal and external customers.
Key Skills:
- Expertise in data architecture and data analysis.
- Strong understanding of data modeling and transformation logic.
- Experience with data pipeline implementation.
- Ability to collaborate with business stakeholders and senior management.
- Proficiency in project management and scope development.
- Knowledge of data quality standards and self-service data solutions.
- Familiarity with machine learning data requirements.
- Excellent communication and relationship-building skills.
Salary (Rate): £36,000 yearly
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: Other
Detailed Description From Employer:
Responsibilities
- Drive completion of data projects and deliverables including the execution of highly complex data solutions while demonstrating best practices, processes and standards for user story development, data analysis, architecture, Modeling, design, coding, and testing.
- Lead or assist in the development, documentation, and maintenance of the enterprise data architecture and its deliverables including the design principles, models, and mappings for transactional and analytical systems serving both strategic and tactical needs.
- Work closely with the business, mapping their most pressing business problems to data and analytic solutions.
- Develop data models and data transformation logic.
- Work with data engineers to implement data pipelines.
- Prototype data solutions to enable the measurement or monitoring of key performance indicators.
- Collaborate with senior management to understand data needs in support of departmental goals.
- Partner with product owners and managers in developing scope, dependencies, estimates, project plans, schedules, status reporting, and issue/risk management.
- Become the subject matter expert in multiple data domains.
- Responsible for defining company standards around data quality.
- Maximize opportunities to implement self-service to allow the business to find the data they need quickly, easily, and accurately.
- Enable data science team to build ML models by designing and providing high quality data.
- Establish and maintain effective and positive relationships with internal and external customers.