Data Analyst at Creative CFO

Job Description

Key Skills and Qualifications:
Bachelor’s degree in Statistics, Data Science, Computer Science, Information Technology, or Engineering.
Proficiency in data analysis tools and languages such as SQL, R, and Python.
Strong SQL skills for data extraction and manipulation.
Proficiency in at least one of Microsoft Azure, Google Cloud Platform/Engine, or AWS Lambda environments.
Familiarity with data visualisation tools such as PowerBI, Pyramid, and Google Looker Studio.
Excellent analytical and problem-solving skills.
Effective communication skills to convey technical findings to non-technical stakeholders.
Eagerness to learn and adapt to new technologies and methodologies

To be successful in the role:
Be excellent with data
Curate and transform diverse datasets to ensure accuracy and reliability.
Collaborate seamlessly with internal teams and customers to maintain data integrity.
Find and provide insightful analysis
Deploy statistical methods and data analysis techniques to reveal trends, patterns, and actionable insights.
Contribute to the development of models and algorithms, extracting meaningful information from complex data sets.
Arrange databases in an efficient manner
Execute SQL queries for efficient data retrieval and manipulation.
Contribute to maintaining and optimising the data warehouse for streamlined storage and retrieval.
Be versatile with technology
Showcase proficiency in SSIS with a focus on effective error handling, logging strategies, and performance tuning.
Utilise expertise in at least one of Microsoft Azure, Google Cloud Platform/Engine, or AWS Lambda environments.
Create strategic reporting and data visualisation
Craft clear and concise reports, dashboards, and visualisations that align with business objectives.
Collaborate with the business intelligence team to enhance reporting capabilities.
Collaborate and communicate at a master level
Work collaboratively with cross-functional teams to understand data needs and provide analytical support.
Effectively communicate technical findings to non-technical stakeholders through engaging presentations and reports.
Focus on quality assurance
Conduct thorough quality assurance checks on data outputs, ensuring accuracy and reliability.
Proactively identify and address data anomalies and discrepancies.
Have a growth mindset and industry awareness
Stay ahead of industry trends, emerging technologies, and best practices in data analysis.
Embrace opportunities for professional development and skill enhancement.