Analytics Engineer (LatAm)

About Homeward

Homeward takes the “what ifs” out of homebuying by creating financial products that give buyers and sellers certainty and convenience. We’re a fast-paced, remote-first real estate startup that partners with real estate agents to offer innovative products and services, helping clients buy with cash, sell with certainty, and time their move, regardless of the market.

Founder and CEO Tim Heyl, owner of one of the fastest-growing agent teams in the country, started Homeward in 2018 to fix the antiquated and broken real estate process. Today, we offer bundled cash offers, mortgage, and title services in 13 states, enabling buyers and sellers to move with certainty and convenience.

We’re a fully remote company with a diverse team spread across several countries - Grounded in three core values: The Golden Rule, One Team One Dream, and Calm Focus. Hear from our employees about what our values mean to them .

About The Opportunity

Homeward is looking for an Analytics Engineer to help build, strengthen, and scale the data foundation that powers decision-making across the business.

This role is ideal for someone who is a confident, self-sufficient data professional with deep experience in data modeling and a passion for turning complex data into reliable, usable products. You’ll own critical pieces of our data pipeline and architecture while partnering closely with analysts, technical teams, and business stakeholders to ensure our data is accurate, dependable, and easy to consume.

A major focus of this role will be data reliability and scalability. We need someone who can identify why pipelines and models are breaking, build solutions that address the root cause, and create an architecture that can scale as our business and use of AI evolve. Most importantly, you take ownership. When you see an opportunity to make our data infrastructure more reliable, scalable, or useful, you're motivated to figure out how to make it better.

You’ll have the opportunity to shape how Homeward structures, governs, and delivers data—enabling analysts, business users, applications, and AI-powered tools to work from consistent and trusted information.

NOTE: Due to current team needs, we are only accepting candidates based in Latin America . This is intended to be a long-term contract position. It is not eligible for U.S. visa sponsorship or relocation.

What You'll Do:

Own the health and reliability of our data pipelines, proactively identifying issues and implementing durable solutions to prevent ingestion failures and downstream disruptions.

Design, develop, test, and optimize scalable dimensional data models and data marts using dbt.

Structure data models around abstract business definitions, creating a reliable connection between business logic and technical implementation.

Help architect our data environment for AI-readiness, ensuring data is structured, documented, governed, and accessible in ways that support emerging AI use cases.

Evaluate and implement tooling and validation processes to ensure dbt models and AI-enabled data workflows produce accurate and trustworthy outputs.

Partner closely with Product and Technology teams on internal application and system changes, identifying upstream and downstream data dependencies and minimizing disruption.

Partner with business stakeholders to understand analytical needs and translate them into scalable solutions within our analytics platform.

Develop deep domain expertise across key areas of Homeward's business and serve as a technical point of contact for associated data models, metrics, and workflows.

Establish monitoring, testing, documentation, and data-quality practices that improve confidence in our data.

Identify opportunities to improve our overall data architecture and recommend practical solutions rather than waiting for requirements to be fully defined.

What You'll Bring:

Strong experience in analytics engineering, data engineering, business intelligence, or a similar data-focused role.

Advanced proficiency in SQL and dimensional data modeling, with demonstrated experience designing scalable analytical datasets.