Analytics Engineer

About the company

They are building the finance-first All-In-One Operating System for Education in Latin America, unifying payments, billing, collections, SIS, academic workflows, communication, and financial operations into a single platform. We serve 1,000+ schools across Mexico, Colombia, and Ecuador, supporting hundreds of thousands of families. With $89M in total capital raised, we continue expanding into new markets and accelerating our product portfolio through strategic acquisitions.

The role

We are looking for an Analytics Engineer Staff/Lead to act as the technical reference of our data team. This is not a traditional BI role: it is a technical leadership position that combines engineering depth with product vision. Your mission is to elevate the data ecosystem maturity at Mattilda, establish standards of excellence, and enable other squads to own their own data.

What you will lead

Analytics Pipeline Architecture: Design and implement the end-to-end data transformation lifecycle using dbt Cloud and Databricks, establishing the patterns and standards that scale with Mattilda's growth.

Silver Layer — Quality Shield: Build and evolve the diagnostic engine that detects Product Gaps, Operational Errors, and Technical Hotfixes at the source, ensuring every manual fix is audited, versioned, and transparently integrated into financial reporting.

Gold Layer — Interface of Truth: Transform technical complexity into high-performance, self-describing Data Products (Recovery Rate, MRR, among others) that allow Mattilda users to navigate information autonomously.

Technical reference for the team: Serve as the technical escalation point, define best practices for modeling, testing, and documentation, and guide Analytics Engineers on the team in their development.

Automation & AI — The Rockstar Project: Lead the transition toward a decentralized data model, developing self-serve standards and spearheading AI agent initiatives and automated workflows that enable engineering squads to build and govern their own data products with zero friction.

What they expect in the first months

90 days: Successful launch of the first certified Data Product backed by automated integrity tests and full documentation.

Infrastructure evolution: Lead the migration from daily snapshots to a real-time CDC architecture.

Organizational impact: Drive a structural shift in team capacity toward product innovation, eliminating the "concierge service" model through intuitive, self-service data products.

Requirements

• 7+ years of experience in Analytics Engineering or Data Engineering, with at least 2 years in technical leadership or staff-level roles. Advanced proficiency in SQL and dbt (models, tests, macros, exposures, documentation).

• Software engineering mindset: Git, CI/CD, unit testing, and documentation-as-code applied to data transformations.

• Proven experience in high-performance modeling: Star Schema, SCD Type 2, handling complex transactional data.

• Experience with Lakehouse architectures (Databricks / Delta Lake).

• Ability to abstract complex business rules (reconciliation, deferred interest, billing cycles) into modular and scalable code.

• Fluency in Spanish and English to collaborate across multi-regional teams and communicate technical strategies to non-technical stakeholders.

Nice to have

• Experience in FinTech or environments with complex billing and collections cycles. Experience integrating and governing data from internal operational tools.

• Strategic vision for building autonomous, decentralized data organizations where success is measured by the level of participation and ownership from product and engineering squads.

• Experience mentoring data teams or serving as a technical reference in high-growth organizations.

Why join

• Architecture from scratch: You lay the foundations of the data ecosystem at a critical inflection point.

• High leverage: Your work directly impacts the reliability of the platform used by thousands of schools and families across LATAM.

• Executive partnership: Work closely with the Head of Data and VP of Engineering to shape their technical architecture.

• Real autonomy: A culture that values engineering excellence and solving root-cause problems, not patching dashboards.

Apply now!

Originally posted on Himalayas