Nearshore Data & Analytics Analyst for Energy Storage & AI Operations teams
Turns plant, field and CRM data into dashboards leadership acts on.
A nearshore data & analytics analyst is a trained, English-fluent professional based in Latin America who works U.S. hours inside your tools. Kurios City recruits, trains and deploys them in about 21 days, at 30–60% lower cost than a U.S.-based hire — the range published for LatAm back-office operations, with free replacement if the person doesn't perform.
What this role owns
- Data models and pipelines
- Dashboards (sales, ops, asset performance)
- Weekly KPI packs
- Data quality checks
- Ad-hoc analysis
Stack we train on
Cost comparison
- Salary$90,000
- Mandatory benefits & payroll taxes$26,713
- Social Security + Medicare, FUTA/SUTA, workers' comp, health insurance, 401(k), PTO
- Tools, management & recruiting$33,287
- AI licenceYou pay it
- If they don't work outYou start over
- One monthly fee$3,700
- Mandatory benefits & payroll taxes$0
- Handled in-country. None of it lands on your payroll.
- Recruiting, training, managementIncluded
- Claude or Codex licenceIncluded
- If they don't work outFree replacement
Example figures for a Data & Analytics Analyst. The U.S. side is fully loaded — what the role actually costs your company, not the salary line. Your real numbers come out of the first call.
| U.S. hire | Kurios nearshore | |
|---|---|---|
| Monthly cost | USD 10,500–14,500 | USD 3,200–4,200 |
| Time to fill | 45–90 days | 21 days |
| Training on your stack | Your team's time | Included |
| Replacement | New search | Free |
| Time-zone overlap | Full | Full (UTC−5 / −6) |
Ranges are indicative. Final figures are scoped per market, role and seniority.
What the first 90 days look like
Week 1–3
Finds where your numbers disagree with each other, and they will. Reconciling the CRM against the accounting system against the project tracker is unglamorous and it has to happen first.
Week 4–8
Builds the three or four reports your leadership team actually reads, on a fixed cadence, from a single source.
Month 3+
Owns the analysis behind decisions — which channels produce profitable projects, where margin leaks, which crews run over. The metric is decisions changed, not dashboards shipped.
How to tell a good data & analytics analyst from a cheap one
Dashboard count is a vanity metric. A good analyst will spend the first month telling you your data is not trustworthy yet, and that is the correct answer even though it is not the one you want. Be suspicious of anyone who promises a live executive dashboard in week two — they will build it on numbers nobody has validated, and you will make a decision on it.
How this role is measured
Decisions changed. Dashboards shipped is a vanity metric. The useful question is how many decisions went a different way because of the analysis.
| What we track | Why it is the right number |
|---|---|
| Reports leadership actually opens | Measured, not assumed |
| Reconciliation gaps closed | Between CRM, accounting and the project tracker |
| Decisions informed | Named, with the decision and the date |
| Time from question to answer | How long leadership waits for a number |
These are the numbers we report back to you, not internal vanity metrics. If one of them stops moving, that is the conversation to have in the weekly check-in.
Available now
No profile available right now — request the role and Kurios sources it in 7–10 days.
Frequently asked
What is the first deliverable worth paying for?
A written list of where your systems disagree with each other. It is unglamorous and it is the only thing that makes every later report trustworthy. Any dashboard built before that reconciliation is a confident answer on unvalidated numbers.
What tools do they work in?
Whatever you already have — Excel and Google Sheets are fine and often correct at your scale. If you have a warehouse, they work in SQL and your BI layer. We do not push a tool purchase you do not need.
How is this different from the Industrial Intelligence Analyst?
This role works on business data: pipeline, margin, cost, throughput. The industrial analyst works on asset and production data from the field. Different data, different domain knowledge, and they are frequently confused when scoping.