Staff Software Engineer, Data Platform – LATAM

Role Overview

• Own the end-to-end architecture for MLS and property data: streaming and batch pipelines, microservices, storage layers, and APIs
• Design and evolve event-driven, Kafka-based data flows that power listing ingestion, enrichment, recommendations, and AI use cases
• Drive technical design reviews, set engineering best practices, and make high-quality tradeoffs around reliability, performance, and cost
• Design, build, and operate backend services (Python or Java) that expose listing, property, and recommendation data via robust APIs and microservices
• Implement scalable data processing with Spark or Flink on EMR (or similar), orchestrated via Airflow and running on Kubernetes where applicable
• Champion observability (metrics, tracing, logging) and operational excellence (alerting, runbooks, SLOs, on-call participation) for data and backend services
• Build and maintain high-volume, schema-evolving streaming and batch pipelines that ingest and normalize MLS and third-party data
• Ensure data quality, lineage, and governance are built into the platform from the start—supporting analytics, AI/ML, and customer-facing features
• Collaborate with ML/AI engineers to design and scale AI agents that automate MLS feed onboarding, listing discrepancy triage, and other operational workflows
• Collaborate closely with Product, Engineering, and Operations to shape the roadmap for our data platform, MLS capabilities, and AI-powered experiences
• Mentor and unblock other engineers; elevate the overall level of technical decision-making on the team via pairing, reviews, and design guidance

Requirements

• 10+ years of professional software engineering experience, including owning production systems end-to-end
• Significant experience working with data-intensive or distributed systems at scale (high volume, high availability)
• Prior experience in a senior or staff/lead role where you influenced architecture, standards, and technical direction
• Strong programming skills in Python or Java, with experience building microservices and APIs (REST/GraphQL)
• Hands-on experience with Apache Kafka or similar event/messaging platforms (Kinesis, Pub/Sub, etc.)
• Deep experience with Spark or Flink for large-scale data processing, across streaming and batch pipelines (on EMR or similar big-data compute)
• Airflow (or equivalent orchestration tools)
• Kubernetes for running data/compute workloads
• Strong SQL and data modeling skills; solid understanding of ETL/ELT patterns, data warehousing concepts, and performance tuning
• Experience building on AWS (preferred) or another major cloud provider, with a good grasp of cost, reliability, and security tradeoffs
• Experience building or integrating AI agents into production workflows (e.g., internal tools, support automation, operational triage, or data workflows)
• Familiarity with frameworks such as PydanticAI, LangGraph, Claude Code or similar, and how they interact with backend services, vector stores, and LLM APIs
• Demonstrated ability to lead technical initiatives across teams, from idea to production (alignment, design, implementation, rollout)
• Track record of mentoring other engineers and raising the bar on code quality, testing, and design
• Strong communication skills; able to clearly explain complex technical decisions to both engineers and non-technical stakeholders
• Customer and product mindset: you care about how the data and services you build improve the end-user and client experience, not just the internals.

Tech Stack

• Airflow
• Apache
• AWS
• Cloud
• Distributed Systems
• ETL
• GraphQL
• Java
• Kafka
• Kubernetes
• Microservices
• Python
• Spark
• SQL

Benefits

• Equal Opportunity Employer
• Professional development opportunities
• Flexible work arrangements