Employment Type: Full-Time
Location: Puerto Rico – 100% Remote within Puerto Rico, USA. Candidates must reside in Puerto Rico and be legally authorized to work in the United States (U.S. citizen or valid U.S. work authorization)
Compensation: $90,000 Base + up to 15% performance bonus + benefits + equity/profit-sharing opportunity
We are seeking a highly analytical and technically capable Financial Analyst – FP&A & Data Analytics who can work directly with large and complex datasets and transform raw financial and operational information into actionable business insights.
This is not a traditional Financial Analyst role centered primarily on Excel reporting, budgeting, and variance explanations.
The ideal candidate will combine a strong FP&A foundation with the technical ability to independently access, query, manipulate, validate, and analyze data using SQL, BI tools, and potentially Python.
We are looking for someone who can take an ambiguous business question and work through the entire analytical process:
Business Question Raw Data SQL/Data Analysis Validation Root-Cause Analysis Financial Impact Dashboard/Model Business Recommendation
The successful candidate will not simply report what happened. They will determine why it happened, what is driving it, what is likely to happen next, and what the business should consider doing about it.
Work hands-on with large financial, operational, customer, vendor, and transactional datasets.
Independently query relational databases using SQL rather than relying exclusively on pre-built reports or other teams to provide data.
Write SQL queries involving multiple tables and data sources to extract the information needed for financial and operational analysis.
Join, filter, aggregate, manipulate, and reconcile data across systems.
Understand database structures, relationships between datasets, and the underlying business logic that determines how data should be interpreted.
Validate data quality and investigate discrepancies between source systems, financial reports, dashboards, and operational data.
Perform detailed data exploration to identify trends, anomalies, correlations, and underlying business drivers.
Work backwards from an ambiguous business problem to determine:
What question needs to be answered.
What data is required.
Where the data resides.
How it should be queried and validated.
What analysis should be performed.
What conclusions can reasonably be drawn.
Build repeatable analytical processes rather than relying on one-time manual analysis.
Automate recurring financial and analytical workflows where appropriate.
Build and maintain dashboards using Power BI, Tableau, or similar BI platforms.
Translate business questions into meaningful KPIs, drill-downs, trends, and performance indicators.
Integrate financial and operational information to provide leadership with a more complete view of business performance.
Design dashboards that allow users to move from high-level KPIs into the underlying drivers of performance.
Identify opportunities to improve existing reporting processes through automation and better data architecture.
Use dashboards as analytical tools for decision-making—not simply as visual representations of historical results.
Python experience is a strong plus.
Candidates with Python experience may use it for:
The successful candidate does not need to be a Data Scientist or Data Engineer, but should be technically curious and comfortable working directly with data.
Technical ability must be combined with a strong financial foundation.
Responsibilities will include:
Budgeting and forecasting.
Budget-to-actual and forecast-to-actual variance analysis.
Financial modeling and scenario analysis.
Cash-flow forecasting.
Revenue and expense analysis.
Profitability analysis.
Unit economics.
KPI development.
Operational-driver analysis.
Long-range financial planning.
ROI and investment analysis.
The Financial Analyst will connect operational activity directly to financial outcomes rather than analyzing financial statements in isolation.
The role will also support more sophisticated forward-looking financial analysis.
This may include:
Cohort analysis.
Vintage analysis.
Customer and portfolio performance.
Unit economics.
Cash-on-cash performance.
Revenue and cost behavior.
Emerging performance trends.
For example, rather than forecasting future performance exclusively from historical averages, the analyst should be able to evaluate how newer customer or portfolio cohorts are performing and determine whether emerging behavior should change the financial forecast.
We are looking for someone who is naturally curious and enjoys investigating why something happened.
For example, if vendor expenses increase materially, this person should be able to independently:
Segment the expense by vendor, category, period, volume, price, or other relevant drivers.
Strong technical skills alone are not enough.
The successful candidate must be able to tell a story with data.
They should be able to identify:
What changed?
Why did it change?
Which drivers actually matter?
Is the change temporary or structural?
What are the financial implications?
What could happen next?
What should management consider doing?
The analyst will regularly translate complex analyses into concise recommendations for Finance, Operations, Product, Technology, and executive leadership.
Bachelor's degree in Finance, Accounting, Economics, Business Analytics, Data Analytics, or a related quantitative discipline.
3–7+ years of experience in FP&A, Financial Analytics, Strategic Finance, Business Intelligence, or a similarly analytical finance role.
Strong hands-on SQL skills.
Experience independently querying and analyzing relational databases.
Demonstrated ability to work with large and complex datasets.
Experience joining and reconciling data across multiple tables or systems.
Strong experience with Power BI, Tableau, or similar BI/analytics tools.
Advanced Excel and financial modeling capabilities.
Strong FP&A fundamentals, including forecasting and variance analysis.
Demonstrated ability to identify trends, anomalies, and root causes within complex datasets.
Ability to translate analysis into clear business recommendations.
Strong communication and presentation skills.
Python for data manipulation, automation, or analytics.
Power Query or similar data-transformation tools.
Experience with cloud data warehouses or modern analytics environments.
Cohort/vintage analysis.
Unit economics.
Transaction-level financial analysis.
FinTech, lending, financial services, SaaS, or another highly data-driven environment.
Experience automating analytical and financial-reporting workflows.
The ideal candidate sits at the intersection of Finance and Data Analytics.
We are not looking for:
A traditional FP&A analyst who depends on someone else to provide the data.
We are also not looking for:
A pure data analyst who can write sophisticated SQL but does not understand the financial or business implications of the analysis.
We are looking for someone who can do both:
Get into the raw data independently, understand it, analyze it, connect it to financial performance, and tell leadership what the business should do with that information.
