Staff Data Engineer - LATAM (Remote)
š GlobalRemote
Posted Mar 27, 2026Updated May 27, 2026
Luxury Presence is building the AI growth platform for real estate. Backed by Bessemer Venture Partners and other top investors, we're a Series C company on track to hit $100M in annual recurring revenue in the next six months. More than 90,000 real estate professionals, including over 30% of the WSJ Real Trends top 100 agents in the United States, use us to run and grow their business.
About the Role
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Weāre seeking a Staff Software Engineer to strengthen our real estate MLS data platform squad. You will build robust data pipelines and backend services that power:
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ā¢ High-quality MLS and property data across 400+ feeds
ā¢ Property discovery and search on agent websites
ā¢ Personalized listing recommendations and other data-driven features
ā¢ Conversational and operational AI agents that streamline internal workflows
ā¢ The evaluation and monitoring infrastructure that keeps these systems improving over time
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This role sits at the intersection of backend engineering, data infrastructure,Ā and AI-powered products.
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Who is the Data Platform Squad?
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We make sure clean, reliable MLS listing records and user click-stream data are always available to our products and customers. Our current teamāa mix of data engineers and software engineersāowns the entire listing pipeline: ingestion, transformation, and normalization across 400+ MLS feeds and other sources.
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We also extend the platform to capture user-activity data for user-facing features such as personalized listing recommendations, and we build AI agents that automate feed onboarding and listing-issue triage, reducing manual effort for internal teams and clients and shortening the path from data to business impact.
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What Youāll Do
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Technical leadership & architecture
ā¢ Own the end-to-end architecture for MLS and property data: streaming and batch pipelines, microservices, storage layers, and APIs
ā¢ Design and evolve event-driven, Kafka-based data flows that power listing ingestion, enrichment, recommendations, and AI use cases
ā¢ Drive technical design reviews, set engineering best practices, and make high-quality tradeoffs around reliability, performance, and cost
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Backend, data & platform engineering
ā¢ Design, build, and operate backend services (Python or Java) that expose listing, property, and recommendation data via robust APIs and microservices
ā¢ Implement scalable data processing with Spark or Flink on EMR (or similar), orchestrated via Airflow and running on Kubernetes where applicable
ā¢ Champion observability (metrics, tracing, logging) and operational excellence (alerting, runbooks, SLOs, on-call participation) for data and backend services
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Streaming & batch data pipelines
ā¢ Build and maintain high-volume, schema-evolving streaming and batch pipelines that ingest and normalize MLS and third-party data
ā¢ Ensure data quality, lineage, and governance are built into the platform from the startāsupporting analytics, AI/ML, and customer-facing features
ā¢ Partner with analytics engineering and data science to make data discoverable and usable (e.g., semantic layers, documentation, self-service tooling)
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AI agents & data products
ā¢ Collaborate with ML/AI engineers to design and scale AI agents that automate MLS feed onboarding, listing discrepancy triage, and other operational workflows
ā¢ Work with frameworks such as PydanticAI, LangChain, or similar to integrate LLM-based agents into our data and service architecture
ā¢ Help define and implement evaluation, logging, and feedback loops so these agents and data-driven products continuously improve
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Cross-functional impact & mentorship
ā¢ Collaborate closely with Product, Engineering, and Operations to shape the roadmap for our data platform, MLS capabilities, and AI-powered experiences
ā¢ Translate ambiguous business and customer problems into clear technical strategies and phased delivery plans
ā¢ Mentor and unblock other engineers; elevate the overall level of technical decision-making on the team via pairing, reviews, and design guidance
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What Youāll Bring
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Experience & scope
ā¢ 10+ years of professional software engineering experience, including owning production systems end-to-end
ā¢ Significant experience working with data-intensive or distributed systems at scale (high volume, high availability)
ā¢ Prior experience in a senior or staff/lead role where you influenced architecture, standards, and technical direction
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Core technical skills
ā¢ 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.)
ā¢ Deep experience with:
Ā ā¦ Spark or Flink for large-scale data processing, across streaming and batch pipelines (on EMR or similar big-data compute)
Ā ā¦ Airflow (or equivalent orchestration tools)
Ā ā¦ Kubernetes for running data/compute workloads
ā¢ Strong SQL and data modeling skills; solid understanding of ETL/ELT patterns, data warehousing concepts, and performance tuning
ā¢ Experience building on AWS (preferred) or another major cloud provider, with a good grasp of cost, reliability, and security tradeoffs
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AI agent experience
ā¢ Experience building or integrating AI agents into production workflows (e.g., internal tools, support automation, operational triage, or data workflows)
ā¢ Familiarity with frameworks such as PydanticAI, LangGraph, Claude Code or similar, and how they interact with backend services, vector stores, and LLM APIs
ā¢ Comfort working with logs, telemetry, and evaluation metrics to monitor, debug, and iteratively improve AI-driven systems
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Leadership & collaboration
ā¢ Demonstrated ability to lead technical initiatives across teams, from idea to production (alignment, design, implementation, rollout)
ā¢ Track record of mentoring other engineers and raising the bar on code quality, testing, and design
ā¢ Strong communication skills; able to clearly explain complex technical decisions to both engineers and non-technical stakeholders
ā¢ 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
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Nice to Have
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ā¢ Experience with any of:
Ā ā¦ Iceberg, Hive, or other table formats/data lake technologies
Ā ā¦ Snowflake, Athena, Redshift, or other cloud data warehouses
Ā ā¦ dbt or similar transformation frameworks
Ā ā¦ Data quality / observability tools (e.g., Great Expectations, Monte Carlo, Datafold)
Ā ā¦ Vector databases / retrieval (e.g., LanceDB, Pinecone, Elasticsearch/OpenSearch)
ā¢ Background in real estate, marketplaces, or other domains where data quality and freshness are highly visible to customers
ā¢ Prior experience in a startup or high-growth environment where youāve built or significantly evolved a data platform