1 – The exact profile of an offshore data analyst who delivers for a French SMB
Hiring a data analyst in Madagascar without precisely defining what you expect is like buying a tool without knowing your problem. The profile of a data analyst for an SMB of 10 to 50 employees has nothing in common with that of a data scientist at a large corporation. Here is what you need to look for, role by role.
1.1: Non-negotiable technical skills
A data analyst dedicated to your SMB must master four building blocks. SQL first: complex queries, multiple joins, subqueries, window functions. That is the foundation. Without solid SQL, everything else falls apart. Excel/Google Sheets next: pivot tables, advanced formulas (INDEX MATCH, ARRAYFORMULA), connection to external sources. Your CFO uses Excel, your data analyst must speak that language fluently. A dataviz tool: Looker Studio, Power BI or Metabase depending on your stack. The candidate must know how to build a dashboard from A to Z, not just modify an existing chart. Python or R as a bonus, but not mandatory for 80% of SMB needs. Do not hire an overqualified profile who will get bored on your monthly reports. Hire the profile that matches your reality. Also verify knowledge of Google Analytics 4 if you have an e-commerce site or a lead-generation website. It has become a prerequisite, not a nice-to-have.
1.2: Business skills that 90% of recruiters overlook
A good data analyst does not just manipulate data. They understand what it means for your business. Look for a profile who has already worked on commercial performance indicators: conversion rate, acquisition cost, average basket, churn. If they have worked with CRM data (HubSpot, Pipedrive, Salesforce), that is a strong signal. If they have produced reports for executives and not just for technical teams, even better. The ability to summarise a result in one sentence that a non-technical executive can understand is worth as much as SQL mastery. Test this skill in the interview: show a dataset, ask for a business interpretation in three lines. If the candidate drowns you in technical jargon, move on to the next one. The ideal profile in Madagascar exists: French-speaking, trained in universities that teach applied statistics, often having worked in local BPOs or digital agencies that already process French client data.
1.3: The standard job description to use for your recruitment
Here are the elements your job description must contain, with no ambiguity. Title: Dedicated Data Analyst. Reporting line: directly to the executive or the sales/marketing manager. Main mission: collect, clean and analyse business data to produce actionable reports. Expected deliverables: weekly dashboards on defined KPIs, monthly analysis reports, automated alerts on critical indicators. Required tech stack: SQL (intermediate-advanced level), Google Sheets or Excel (advanced level), one dataviz tool (specify yours), GA4, knowledge of a CRM. Language: fluent French written and spoken. Availability: full-time, hours aligned with France (the time difference with Madagascar is +1h in summer, +2h in winter, so almost none). What makes this different from a standard job description: you specify the exact tools the candidate will use. No vague wording like "BI tools". You name Metabase, or Looker Studio, or Power BI. Le vivier de talents francophones à Antananarivo makes it possible to find these profiles, provided the search is properly framed.
2 – The technical testing grid to avoid hiring a CV
A data analyst's CV in Madagascar can list SQL, Python, Power BI. The problem: between "listed on a CV" and "mastered in production", there is a chasm. Here are the three tests that TARAM administers to every data analyst candidate before presenting them to the client.
2.1: SQL test under real conditions (45 minutes, supervised)
Not a multiple-choice quiz. A test on a real, or at least realistic, database. Provide three tables (clients, orders, products) with 5,000 to 10,000 rows. Set five queries of increasing difficulty. Query 1: simple extraction with filter and sort. Query 2: join between two tables with aggregation (revenue per client over the last 6 months). Query 3: subquery or CTE to identify clients whose average basket exceeds the median. Query 4: window function to calculate the month-by-month change in an indicator. Query 5: an open-ended question such as "Which clients are at risk of churning in the next 30 days?" where the candidate must choose their own approach. The test lasts 45 minutes, timed. The candidate works in an environment you control (an online PostgreSQL instance, a BigQuery sandbox). You evaluate syntax, logic, speed, and above all the ability to interpret the result. A candidate who returns a table with no commentary fails, even if the query is correct.
2.2: Dataviz test on your own tool (60 minutes)
Give the candidate a CSV export of anonymised real data (or a fictional dataset modelled on your activity). Ask them to build a dashboard with three visualisations: a global performance indicator (KPI card), a time-series chart, a filterable detail table. The test is done on your tool: if you use Looker Studio, the test is done on Looker Studio. If you use Power BI, the same applies. No substitution. You evaluate four things. The choice of visualisations (a pie chart for 15 categories is an instant elimination). Readability (titles, legends, units). Interactivity (filters, segments). And the accompanying note: the candidate must write a paragraph in French explaining what the dashboard reveals, addressed to an executive. This test eliminates 40% of candidates who master SQL but cannot tell a story with data. That is exactly the filter you need. La même rigueur s'applique quand vous recrutez un développeur front-end offshore: without a test on your real stack, you are hiring blind.
2.3: Business interview: the test nobody does that changes everything
After the technical tests, spend 30 minutes on a video call with the candidate. Not to verify their SQL, that is done. To verify that they understand your business. Present your activity to them in 5 minutes. Show them a raw dataset (a CRM export, a sales table). Ask them three questions. "Which indicators would you prioritise and why?" "This table shows a 15% drop in conversion rate in March. What hypotheses do you form?" "How would you present this result to an executive who has only 2 minutes?" The right candidate asks clarifying questions before answering. They structure their response. They prioritise. The wrong candidate recites a list of tools or gets lost in technical details. This business interview is the final filter. It separates the technical executor from the data analyst who will genuinely help you make better decisions. At TARAM, this test is systematic. The candidate only reaches the shortlist if they pass all three assessments. The client always validates the final profile.
3 – Integration into your analytics stack in 3 weeks
Hiring the right profile is not enough. If integration takes three months, you have lost the advantage. Here is the 3-week onboarding plan that TARAM applies to make a data analyst operational on your real data, with your tools, within your processes.
3.1: Week 1 — access, documentation and first delivery
Day 1: the data analyst receives their access credentials. Database, CRM, dataviz tool, Google Analytics, Slack or Teams for your team. Not in three days. On day one. TARAM provides the infrastructure (Ryzen 7 workstation, dual fibre + 5G connection), the client provides application access. Days 2-3: the data analyst reads your existing documentation. Data dictionary if you have one. If not, they create one. They map your data sources: which tables, which fields, which connections between CRM, website, ERP. Days 4-5: first delivery. A simple but functional dashboard, on a scope you defined together (for example: tracking current month leads). This first delivery has two objectives. Verifying that access works and that the data is usable. And creating a first concrete exchange around a real deliverable, not theory. If your data analyst delivers a usable dashboard in 5 days, you know the hire worked. La structuration du ramp-up est ce qui sépare les 27 % qui réussissent des 73 % qui échouent.
3.2: Week 2 — business immersion and reporting automation
The data analyst has understood your data. Now they must understand your business. Schedule two 45-minute sessions with key people: the executive, the sales manager, the CFO if there is one. Each session has one objective: what are your critical indicators, how often do you consult them, and what decision do you make when an indicator deviates. The data analyst translates these exchanges into dashboard specifications. In parallel, they automate. The manual exports you did every Monday morning become scheduled queries. The Excel tables you filled in by hand become dashboards fed in real time. By the end of week 2, you should have two or three automated reports replacing tasks you used to do yourself. That is the first measurable time saving. If you are managing this profile without an intermediary manager, le stack minimal pour piloter une équipe dédiée à Madagascar sans daily meeting gives you the exact tools and rituals.
3.3: Week 3 — autonomy, alerts and first data-driven decision cycle
In week 3, the data analyst operates autonomously. They produce their deliverables without you having to ask for them. They have configured automatic alerts: if the conversion rate drops below a threshold, if the acquisition cost exceeds an amount, if a client segment disengages. These alerts arrive in Slack or by email, with an interpretive comment. Not just a number. A sentence such as: "The conversion rate on the 10-20 employee SMB segment has dropped 22% this week. Hypothesis: the Google Ads campaign targeting this segment was paused on Wednesday." By the end of week 3, you make your first business decision backed by a deliverable produced by your offshore data analyst. Reactivating a campaign, adjusting a price, re-engaging a segment. That is the moment the investment becomes tangible. A dedicated team member, integrated into your tools, working exclusively for you, at a cost three times lower than a hire in France. TARAM has not sold you a service. TARAM has integrated an analytical capability into your business. Comparez cette approche à une internalisation classique: the differential speaks for itself.
Your competitor already has their dashboards. You still have your Excel exports.
Every week without a dedicated data analyst, you make decisions based on gut feeling while others rely on clean data. The profile exists in Antananarivo. The testing grid exists in this article. The 3-week integration plan is documented. What is missing is your decision. Keep stacking Excel files and losing hours on manual reports, or integrate a dedicated data analyst who works exclusively for you, in your tools, on your data, with structured management from Maurice. At TARAM, 1 team member = 1 client. No pooling, no rotation, no surprises. Three weeks between your call and the first dashboard in production. The next quarter starts with or without you.
