Job description: Data Analyst in Kenya
Duties, requirements, interview questions and a template ready for you to advertise your vacancy today.
Role summary
A data analyst collects, cleans and interprets the data of a business so that managers can make decisions on evidence instead of instinct. The role builds reports and dashboards, investigates why figures move and explains the findings in plain language. A good analyst exposes unprofitable products, wasted spending and missed opportunities that nobody could see in the raw spreadsheets.
Main duties
- Extract data from databases, spreadsheets and business systems using SQL and other query tools
- Clean and validate datasets, resolving duplicates, gaps and inconsistencies before any analysis
- Build and maintain dashboards in tools such as Power BI, Tableau or Looker Studio
- Produce recurring reports on sales, costs, customers and operations for each department
- Investigate changes in key indicators and identify the causes behind trends and anomalies
- Translate business questions from managers into measurable analyses with clear definitions
- Present findings and recommendations to non-technical audiences with simple, honest charts
- Automate repetitive reports with scripts, scheduled queries or spreadsheet macros
- Document data sources, metric definitions and assumptions so that the figures can be trusted
Typical requirements
- Degree in statistics, mathematics, economics, computing, engineering or a related field
- One to three years of experience in data analysis or business intelligence
- Strong SQL and advanced Excel, including pivot tables and complex formulas
- Experience building dashboards in at least one business intelligence tool
- Working knowledge of Python or R for analysis is an advantage
- Sound grasp of basic statistics and the ability to explain results in clear English
Key skills
Vacancy template ready to copy
Replace the fields in square brackets with your company details.
Data Analyst — [Company name]
At [Company name] we are looking for a data analyst to help us make better decisions from our office in [City]. The role involves preparing data, building dashboards and investigating the questions that matter to the business, working with managers across sales, finance and operations.
Responsibilities
- Extract data from databases, spreadsheets and business systems using SQL and other query tools
- Clean and validate datasets, resolving duplicates, gaps and inconsistencies before any analysis
- Build and maintain dashboards in tools such as Power BI, Tableau or Looker Studio
- Produce recurring reports on sales, costs, customers and operations for each department
- Investigate changes in key indicators and identify the causes behind trends and anomalies
- Translate business questions from managers into measurable analyses with clear definitions
- Present findings and recommendations to non-technical audiences with simple, honest charts
- Automate repetitive reports with scripts, scheduled queries or spreadsheet macros
- Document data sources, metric definitions and assumptions so that the figures can be trusted
Requirements
- Degree in statistics, mathematics, economics, computing, engineering or a related field
- One to three years of experience in data analysis or business intelligence
- Strong SQL and advanced Excel, including pivot tables and complex formulas
- Experience building dashboards in at least one business intelligence tool
- Working knowledge of Python or R for analysis is an advantage
- Sound grasp of basic statistics and the ability to explain results in clear English
What we offer
- Salary: [Salary]
- Benefits: [Benefits]
- Location: [City]
Interested in this vacancy? Apply today.
How to hire a data analyst
Start from the business questions
Before writing the advert, it helps to list the five decisions the analyst should inform. That list defines whether the need is reporting, deeper analysis or data engineering, which are three different profiles.
Test SQL with realistic data
A short exercise on a small, messy dataset, with duplicates and missing values, shows whether the candidate checks data quality before answering. Clean textbook exercises hide exactly that weakness.
Ask for an explanation, not a chart
The candidate should present one past analysis as if to a managing director with five minutes to spare. Analysts who cannot reach the conclusion quickly will produce dashboards that nobody uses.
Check the state of the data
Where company data is scattered across spreadsheets, the first months will be spent on cleaning and structure. Saying so honestly attracts candidates who enjoy that work and discourages those expecting ready-made data.
Do not confuse analyst and scientist
Asking for machine learning when the need is reliable monthly reporting leads to an overqualified and soon frustrated hire. Most small businesses benefit first from a solid analyst with strong SQL and business sense.
Interview questions for data analyst roles
Could you describe an analysis of yours that changed a business decision?
What to look for: A clear question, a sound method and a measurable outcome, not just a report delivered.
What do you check first when you receive a new dataset?
What to look for: Row counts, duplicates, missing values, date ranges and whether definitions match the business.
What would you do if two reports showed different totals for the same sales figure?
What to look for: Traces both back to source, compares filters and definitions and agrees a single version.
How would you explain the difference between a mean and a median to a sales manager?
What to look for: A simple, concrete example showing why outliers distort the mean.
How would you write a query to find customers who bought last year but not this year?
What to look for: Correct use of joins or subqueries and attention to date boundaries and duplicates.
How did you handle a request that was vague, such as being asked for a report on customers?
What to look for: Asks what decision the report supports before building anything.
What would you do if your analysis contradicted what a senior manager believed?
What to look for: Rechecks the work, presents evidence clearly and respectfully and stays open to missing context.
Which reports have you automated, and how much time did that save?
What to look for: Specific tools and results, and care to monitor the automated output for errors.
Shortlist better with the Saplic ATS
Posting the vacancy is the first step. The Saplic recruitment system organises everything that comes afterwards.
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Frequently asked questions
What are the requirements for a data analyst in Kenya?
The most common ones in vacancies are: degree in statistics, mathematics, economics, computing, engineering or a related field; one to three years of experience in data analysis or business intelligence; strong SQL and advanced Excel, including pivot tables and complex formulas.
Does a small business really need a data analyst?
It becomes worthwhile when managers spend hours each week assembling spreadsheets, or when important decisions on pricing, stock or marketing are made without reliable figures. Below that point, training an existing employee in spreadsheets and a dashboard tool may be sufficient for the time being.
What practical test is suitable for a data analyst?
Provide a small anonymised dataset and one business question, and allow about an hour. Assess whether the candidate cleans the data, reaches a defensible answer and explains it simply. The quality of the reasoning and of the communication matters more than the sophistication of the technique.
How much does it cost to advertise data analyst vacancies on Saplic?
Nothing. Posting the vacancy is free and no credit card is required. The recruitment system for organising the people who apply is free as well.