Luxury Presence is hiring a Staff Software Engineer to join our Data Platform Squad. You will be a key technical leader building robust data pipelines and backend services that power high-quality MLS and property data across 400+ feeds. This role sits at the intersection of backend engineering, data infrastructure, and AI-powered products.

What You'll Do

Own the end-to-end architecture for MLS and property data, including 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 in 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 and operational excellence for data and backend services, including alerting, runbooks, SLOs, and on-call participation.
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 to support analytics, AI/ML, and customer-facing features.
Partner with analytics engineering and data science to make data discoverable and usable through semantic layers, documentation, and 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 AI-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 to elevate the overall level of technical decision-making on the team.

What We're Looking For

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.
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.
Hands-on experience with Apache Kafka or similar event/messaging platforms.
Deep experience with Spark or Flink for large-scale data processing across streaming and batch pipelines.
Deep experience with Airflow or equivalent orchestration tools.
Deep experience with Kubernetes for running data/compute workloads.
Strong SQL and data modeling skills and a solid understanding of ETL/ELT patterns and data warehousing concepts.
Experience building on AWS or another major cloud provider, with a good grasp of cost, reliability, and security tradeoffs.
Experience building or integrating AI agents into production workflows.
Familiarity with frameworks such as PydanticAI, LangGraph, or similar and how they interact with backend services 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.
Track record of mentoring other engineers and raising the bar on code quality, testing, and design.
Strong communication skills and the ability to clearly explain complex technical decisions to both engineers and non-technical stakeholders.
Customer and product mindset focused on how your work improves the end-user and client experience.

Nice to Have

Experience with Iceberg, Hive, or other table formats/data lake technologies.
Experience with Snowflake, Athena, Redshift, or other cloud data warehouses.
Experience with dbt or similar transformation frameworks.
Experience with data quality or observability tools like Great Expectations or Monte Carlo.
Experience with vector databases or retrieval systems like LanceDB or Pinecone.
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.