Job Description
The Finance Data Analytics Lead is responsible for helping businesses leverage data and identify opportunities for improvement, spot trends, as well as recognize potential issues and offer solutions.
However, the primary duty of the Finance Data Analytics Lead is to transform data into insight that can help to improve the value of the business.

Description:
Should have strong analytical skills and problem-solving attitude.
· Strong interpersonal communication, collaboration with the team, and presentation skills required.
· Excellent communication skills with a demonstrated ability to explain complex concepts to non-technical stakeholders.
· knowledge of MS SQL and T-SQL Programming Skills.
· Interest in data analysis.
· Ability to manage time and resources and juggle competing deadlines effectively.
· Establish and maintain KPIs to monitor performance and optimize reporting processes for continuous improvement.
· Familiar with Python programming language.
· Familiar with statistical analysis techniques and data mining algorithms.
· Works with multiple data sources, utilizing data visualization tools (Power BI Desktop or any other visualization tools).
QUALIFICATIONS, EXPERIENCE & SKILLS:
Minimum academic and professional qualifications:
· Bachelor's or Master's degree in related fields.
Experience:
· 5+ years of experience in a similar role.
· Proven track record in managing complex projects and driving growth initiatives.
· Experience in e-commerce, fintech, or martech industries is highly desirable.
Technical Skills:
· English Language.
· Good MS Office skills particularly strong capabilities in MS Excel
· Good python, SQL, power BI skills.
· Analyzing business requirements and processes and recommending them to the management and executives for implementation.
· Creating and maintaining documentation that includes the design, requirements and user manuals for the organization.
· Identifying the development needs for the purpose of streamlining and improving the operations of the organization for efficiency and profitability.
· Finding patterns or seeing areas in the data that signal a potential for improvement in business practices.
· Establishing best practices for data management to ensure accuracy and consistency.
· Gathering end-user reporting and dashboard requirements.
· Creating dashboards and reports and supporting users with them