Staff Software Engineer, Data Platform - US (Remote)

Luxury Presence-posted 1 day ago
Full-time • Mid Level
Remote
501-1,000 employees
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We’re 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: • High-quality MLS and property data across 400+ feeds • Property discovery and search on agent websites • Personalized listing recommendations and other data-driven features • Conversational and operational AI agents that streamline internal workflows • The evaluation and monitoring infrastructure that keeps these systems improving over time This role sits at the intersection of backend engineering, data infrastructure, and AI-powered products. Who is the Data Platform Squad? We make sure clean, reliable MLS listing records and user click-stream data are always available to our products and customers. Our current team—a mix of data engineers and software engineers—owns the entire listing pipeline: ingestion, transformation, and normalization across 400+ MLS feeds and other sources. 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.

  • 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
  • Partner with analytics engineering and data science to make data discoverable and usable (e.g., semantic layers, documentation, self-service tooling)
  • Collaborate with ML/AI engineers to design and scale AI agents that automate MLS feed onboarding, listing discrepancy triage, and other operational workflows
  • Work with frameworks such as PydanticAI, LangChain, or similar to integrate LLM-based agents into our data and service architecture
  • Help define and implement evaluation, logging, and feedback loops so these agents and data-driven products continuously improve
  • Collaborate closely with Product, Engineering, and Operations to shape the roadmap for our data platform, MLS capabilities, and AI-powered experiences
  • Translate ambiguous business and customer problems into clear technical strategies and phased delivery plans
  • Mentor and unblock other engineers; elevate the overall level of technical decision-making on the team via pairing, reviews, and design guidance
  • 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
  • Comfort working with logs, telemetry, and evaluation metrics to monitor, debug, and iteratively improve AI-driven systems
  • 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
  • Experience with any of: ◦ Iceberg, Hive, or other table formats/data lake technologies ◦ Snowflake, Athena, Redshift, or other cloud data warehouses ◦ dbt or similar transformation frameworks ◦ Data quality / observability tools (e.g., Great Expectations, Monte Carlo, Datafold) ◦ Vector databases / retrieval (e.g., LanceDB, Pinecone, Elasticsearch/OpenSearch)
  • Background in real estate, marketplaces, or other domains where data quality and freshness are highly visible to customers
  • Prior experience in a startup or high-growth environment where you’ve built or significantly evolved a data platform
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