Senior Data Analyst
Our client is looking for a Senior Data Analyst to be working in the United States.
The data analyst collects, organises and studies data to provide business insight.
Responsibilities of the Senior Data Analyst:
- Seek to understand the needs of business users.
- Maintain and build curated, flexible, datasets and dashboards that support teams across the company.
- Enable and encourage data users to explore and gain insights from those datasets and dashboards through consultative approach, training and documentation.
- Collaborate with data engineering to contribute to the development of data assets.
- Learn how to identify and resolve data disparity issues independently, as appropriate.
- Respond to ad-hoc queries and implement solutions to enable non-technical end-users to answer, independently, in the future.
- Use data science techniques to find data patterns, anomalies, and optimization opportunities.
- Maintain a library of analytical code (SQL, R, Python, etc.) and data visualization dashboards (PowerBI, etc.).
- Provide expertise on mathematical concepts for the broader applied analytics team and inspire the adoption of advanced analytics and data science across the entire organisation.
- Interpret, translate and communicate analytical findings to business stakeholders through visualizations.
- High level of accountability, customer-focused, consultative approach with stakeholders, comfort building expertise w/ new tools, and the ability to independently execute against goals.
- Strong verbal and written communication skills with demonstrated ability to communicate professionally at all levels including executive management.
- Experience creating highly adopted, and intuitive dashboards with BI tools like Power BI, Qlik, or Tableau.
- Strong project management and organizational skills.
- High level of proficiency w/ SQL and relational databases.
- An understanding of how to apply statistical, and data warehousing concepts.
- Bachelor’s Degree in Computer Science, MIS, Mathematics, Statistics, Economics, and/or equivalent work experience in a related field.
- Experience in using data science techniques to find data patterns, anomalies, and optimization opportunities.
- Basic programming skills in Python, R.
- Experience with Data Science tools like Knime, Jupyter, MATLAB, etc.
- Exposure to ESG Analytics.