Finance Analytics Analyst

What is Cobre, and what do we do?

Cobre is Latin America’s leading instant b2b payments platform. We solve the region’s most complex money movement challenges by building advanced financial infrastructure that enables companies to move money faster, safer, and more efficiently.

We enable instant business payments—local or international, direct or via API—all from a single platform.

Built for fintechs, PSPs, banks, and finance teams that demand speed, control, and efficiency. From real-time payments to automated treasury, we turn complex financial processes into simple experiences.

Cobre is the first platform in Colombia to enable companies to pay both banked and unbanked beneficiaries within the same payment cycle and through a single interface.

We are building the enterprise payments infrastructure of Latin America!

What we are looking for:

We're looking for a mid-level Finance Analytics Analyst who lives at the intersection of finance and data. This is not a traditional Excel-only FP&A role, nor a pure data engineering role: it's someone who understands the financial business of a payments fintech and who can build, hands-on, the data models and analytics that drive finance decisions.

The role is roughly 60% FP&A / financial analysis and 40% data (SQL/dbt modeling, pipelines, visualization). You'll work closely with the finance team, including leadership (CFO), and with cross-functional teams (RevOps, Product, Operations).

What would you be doing:

Finance & FP&A 

• Build and maintain monthly financial reporting: P&L, unit economics, margins by product, volume mix and contribution.
• Client- and product-level revenue analysis: concentration, churn, repricing, LTV/CAC, contribution margin.
• Model costs and margins (e.g. cross-border costs by corridor, settlement rail and partner costs).
• Support the planning, forecast and close cycles, connecting the numbers to the business story behind them.
• Prepare reports and executive readouts for finance leadership.

Data & analytics 

• Write robust SQL on Snowflake and build/maintain dbt models (staging, intermediate, marts).
• Automate today's manual extractions and pipelines (e.g. accounting sources, cost models, FX).
• Design dashboards and views in BI tools so finance and stakeholders can self-serve.
• Own data quality: deduplication, currency conversion logic to USD, reconciliations, clear metric definitions.
• Use Python for ad-hoc analysis, automation and data wrangling that SQL doesn't handle well.

What do you need:

• 3+ years of experience in data analytics, business intelligence, analytics engineering, or a similar role, ideally supporting Finance, FP&A, Revenue, or Operations teams.
• Advanced SQL skills, including CTEs, window functions, and query optimization over large datasets.
• Experience working with Snowflake or another modern cloud data warehouse.
• Hands-on experience building and maintaining dbt models or equivalent data transformation frameworks.
• Proficiency in Python for analysis, automation, and data wrangling (e.g., pandas).
• Experience with BI and visualization tools such as Sigma, Looker, Tableau, Power BI, or similar.
• Solid understanding of financial concepts such as P&L, margins, unit economics, and revenue, with the ability to apply them in analytical contexts.
• Ability to translate business questions into data-driven analyses and communicate insights clearly to technical and non-technical stakeholders.
• Professional proficiency in both English and Spanish (written and spoken).

Nice-to-have

• Experience in fintech, payments or financial services.
• dbt and analytics engineering practices (testing, documentation, version control).
• Familiarity with FX / multi-currency (COP, MXN, USD) and conversion logic.
• Exposure to accounting systems (e.g. NetSuite) and close processes.
• Git and collaborative work on code.

What success looks like

First 90 days: you've mastered the financial data model and delivered your first end-to-end report/dashboard autonomously.

6 months: you own key lines of the monthly reporting and have automated at least one process that is manual today.