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.