Job summary

Minimum of 5 years’ experience in data analysis or analytics roles

Min Qualification:

Bachelors

Experience Level:

Mid level

Experience Length:

5 years

Language Requirement:

English

Working Hours:

Full Time - 9 to 5

Applicant Location:

Nairobi, Kenya

Job descriptions & requirements

1. Education & Experience

Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related field

Minimum of 5 years’ experience in data analysis or analytics roles

Proven experience working with:

• Large and complex datasets
• Cross-functional teams (product, finance, operations, technology)

Experience in financial services, fin tech, or lending environments is a strong advantage

2. Technical Skills (Non-Negotiable)

Data Handling & Querying

Advanced SQL,

Experience with relational databases such as PostgreSQL or MySQL

Experience with advanced excel

Programming

Strong proficiency in Python (preferred) or R

Experience with data analysis libraries such as Pandas and NumPy

Familiarity with Scikit-learn or similar tools is an added advantage

Data Visualization & BI

Hands-on experience with Power BI or Tableau

Ability to:

• Build and maintain dashboards
• Design KPI tracking systems
• Present insights in a clear and compelling manner

Data Modeling & Warehousing

Strong understanding of data modeling concepts (e.g., star and snowflake schemas)

Experience with ETL/ELT processes

Familiarity with tools such as dbt, Airflow, or equivalent

3. Analytical & Business Skills

Ability to translate business problems into structured data analyses

Strong experience defining, tracking, and interpreting KPIs

Proficiency in root cause analysis and problem diagnosis

Experience with:

• Cohort analysis
• Funnel analysis
• Forecasting and trend analysis

Understanding of:

• A/B testing methodologies
• Statistical significance and interpretation

4. Problem-Solving & Critical Thinking

Ability to work with ambiguous or incomplete data

Strong capability in identifying and resolving data quality issues

Structured approach to solving complex and unstructured problems

High attention to detail, particularly in financial or operational contexts

5. Communication & Stakeholder Management

Ability to present insights clearly to non-technical stakeholders

Proven ability to influence decision-making using data

Skilled in simplifying complex analyses into actionable insights

Experience working with:

• Senior leadership
• Product and operations teams

6. Data Governance & Quality

Experience with:

• Data validation and cleaning processes
• Data integrity checks
• Documentation of data definitions and standards

Ability to:

• Establish and maintain data quality frameworks
• Ensure consistency and reliability across multiple systems

7. Leadership & Ownership

Ability to own analytics initiatives end-to-end

Experience mentoring junior analysts

Demonstrated ability to promote a data-driven culture

Experience leading:

• Reporting automation initiatives
• Dashboard standardization efforts

8. Nice-to-Have Skills

Exposure to machine learning concepts and techniques

Experience with APIs and data integrations

Familiarity with cloud platforms such as AWS, GCP, or Azure

Additional exposure to:

• Real-time analytics systems
• Fraud detection or risk analytics environments

• Advanced SQL
• Relational databases (PostgreSQL, MySQL)
• Advanced Excel
• Python (preferred) or R
• Pandas
• NumPy
• Scikit-learn (added advantage)
• Power BI or Tableau
• Data modeling concepts
• ETL/ELT processes
• dbt, Airflow, or equivalent (familiarity)
• KPI definition, tracking, and interpretation
• Root cause analysis
• Cohort analysis
• Funnel analysis
• Forecasting and trend analysis
• A/B testing methodologies
• Statistical significance and interpretation
• Problem-solving
• Critical thinking
• Data quality issue identification and resolution
• Communication
• Stakeholder management
• Data validation and cleaning
• Data integrity checks
• Data governance
• Leadership
• Mentoring junior analysts
• Machine learning concepts (exposure)
• APIs and data integrations (experience)
• Cloud platforms (AWS, GCP, Azure - familiarity)
• Real-time analytics systems (additional exposure)
• Fraud detection or risk analytics environments (additional exposure)

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related field
• Minimum of 5 years’ experience in data analysis or analytics roles
• Proven experience working with large and complex datasets
• Proven experience working with cross-functional teams (product, finance, operations, technology)
• Experience in financial services, fin tech, or lending environments is a strong advantage
• Advanced SQL
• Experience with relational databases such as PostgreSQL or MySQL
• Experience with advanced excel
• Strong proficiency in Python (preferred) or R
• Experience with data analysis libraries such as Pandas and NumPy
• Familiarity with Scikit-learn or similar tools is an added advantage
• Hands-on experience with Power BI or Tableau
• Ability to build and maintain dashboards
• Ability to design KPI tracking systems
• Ability to present insights in a clear and compelling manner
• Strong understanding of data modeling concepts (e.g., star and snowflake schemas)
• Experience with ETL/ELT processes
• Familiarity with tools such as dbt, Airflow, or equivalent
• Ability to translate business problems into structured data analyses
• Strong experience defining, tracking, and interpreting KPIs
• Proficiency in root cause analysis and problem diagnosis
• Experience with cohort analysis
• Experience with funnel analysis
• Experience with forecasting and trend analysis
• Understanding of A/B testing methodologies
• Understanding of statistical significance and interpretation
• Ability to work with ambiguous or incomplete data
• Strong capability in identifying and resolving data quality issues
• Structured approach to solving complex and unstructured problems
• High attention to detail, particularly in financial or operational contexts
• Ability to present insights clearly to non-technical stakeholders
• Proven ability to influence decision-making using data
• Skilled in simplifying complex analyses into actionable insights
• Experience working with senior leadership
• Experience working with product and operations teams
• Experience with data validation and cleaning processes
• Experience with data integrity checks
• Experience with documentation of data definitions and standards
• Ability to establish and maintain data quality frameworks
• Ability to ensure consistency and reliability across multiple systems
• Ability to own analytics initiatives end-to-end
• Experience mentoring junior analysts
• Demonstrated ability to promote a data-driven culture
• Experience leading reporting automation initiatives
• Experience leading dashboard standardization efforts
• Exposure to machine learning concepts and techniques
• Experience with APIs and data integrations
• Familiarity with cloud platforms such as AWS, GCP, or Azure
• Additional exposure to real-time analytics systems
• Additional exposure to fraud detection or risk analytics environments