Senior Data Engineer (LATAM)

Lateral Group • Montevideo, LATAM

7d

About The Position

The Senior Data Engineer will join a small, senior Data & Analytics team at a real estate investment firm with a Snowflake data platform integrating more than 40 data sources. The role focuses on assessing, stabilizing, and improving the existing platform, making reporting reliable, and automating manual work so teams can focus on investment and operating decisions.

The work supports transaction decisions such as buying, selling, commercial leasing, and valuation; reporting and source-of-truth needs such as benchmarks, fund and asset performance, and management reporting; and day-to-day business decisions by improving access to information. The engineer will work directly with client technical and business teams, diagnose platform issues, prioritize improvements, and raise quality standards through code reviews, documentation, and constructive pushback.

The role calls for strong technical judgment, end-to-end ownership, clear communication with non-engineers, and practical use of AI in engineering work. The company describes its working style as self-managed and distributed, with asynchronous collaboration, evolving priorities, and a focus on sustainable, high-quality delivery.

Requirements

• At least 5 years of data engineering experience.
• Experience owning a production data platform, rather than only contributing to someone else's.
• Hands-on Snowflake experience, including warehouse sizing and cost management, MERGE patterns, streams and tasks, and clustering.
• Experience diagnosing and fixing pipelines built by others, including slow queries, unexplained costs, and silent data quality failures.
• Experience designing, implementing, and maintaining ETL/ELT pipelines and data integration across many sources.
• Strong data modeling skills, including defining and enforcing grain, layering data from raw through staging to marts, and handling corrections and late-arriving data.
• Advanced SQL skills, including window functions and query performance work.
• Experience with a transformation framework such as dbt or equivalent and an orchestrator such as Airflow, Dagster, or similar.
• A working relationship with AI that is reflected in how work is built, not only in tools tried.
• Ability to own work end to end, from design through deployment and support.
• A quality mindset, including clean code, good documentation, thoughtful testing, and attention to data quality and lineage.
• Strong English communication and asynchronous collaboration skills, with a proactive approach to problems.
• Ability to understand the business context behind data and make sound prioritization decisions.

Nice To Haves

• Asset management or commercial real estate experience.
• Familiarity with Yardi, MRI, RealPage, Argus, VTS, or CoStar.
• Experience building analytics-ready models for BI tools, particularly Power BI.
• Experience with AWS Glue or EMR alongside Snowflake.
• Exposure to workflow automation tools such as Power Automate or UiPath.
• A track record of mentoring other engineers.

Responsibilities

• Assess and stabilize an existing Snowflake data platform integrating more than 40 data sources.
• Diagnose and resolve issues in inherited pipelines, including slow queries, unexplained costs, and silent data quality failures.
• Improve the reliability of reporting and automate manual work.
• Design, implement, and maintain ETL/ELT pipelines and integrations across multiple sources.
• Build and maintain strong data models, including clear grain, layered data from raw through staging to marts, and handling corrections and late-arriving data.
• Manage Snowflake warehouses, costs, MERGE patterns, streams and tasks, and clustering.
• Use advanced SQL for data transformations and query performance improvements.
• Own work end to end, from design through deployment and support.
• Support transaction, reporting, and day-to-day business decision-making with reliable data access and models.
• Work directly with technical and business teams and explain engineering priorities to non-engineers.
• Raise engineering standards through code review, documentation, testing, and attention to data quality and lineage.
• Use AI practically in the way data engineering work is built.
• Collaborate proactively in a distributed, asynchronous environment and adapt as priorities and processes evolve.

Benefits

• Meaningful work on products intended to make a measurable difference.
• Support for growing professional craft, exploring new paths, and taking on greater responsibility at the employee's own pace.
• A sustainable approach to high-quality work, with the company stating there is no burnout or crunch.
• A profitable, independent company that has been operating for more than two decades, offering stability and varied project challenges.
• A collaborative team culture described as talented, kind, hardworking, and low-ego.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed