Staff Software Engineer, Data Platform - US (Remote)

Luxury Presence

48d•Remote

About The Position

Weâre 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: â¢ High-quality MLS and property data across 400+ feeds â¢ Property discovery and search on agent websites â¢ Personalized listing recommendations and other data-driven features â¢ Conversational and operational AI agents that streamline internal workflows â¢ 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 teamâa mix of data engineers and software engineersâowns 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.

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: â¦ Spark or Flink for large-scale data processing, across streaming and batch pipelines (on EMR or similar big-data compute) â¦ Airflow (or equivalent orchestration tools) â¦ 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

Nice To Haves

Experience with any of: â¦ Iceberg, Hive, or other table formats/data lake technologies â¦ Snowflake, Athena, Redshift, or other cloud data warehouses â¦ dbt or similar transformation frameworks â¦ Data quality / observability tools (e.g., Great Expectations, Monte Carlo, Datafold) â¦ 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 youâve built or significantly evolved a data platform

Responsibilities

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 startâsupporting 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