Responsibilities
- Lead the full lifecycle design and management of data systems for MLS and property information, covering streaming and batch processing, microservices, storage, and API layers
- Create and refine event-driven data architectures using Kafka to support listing ingestion, data enrichment, recommendation engines, and artificial intelligence applications
- Lead technical design evaluations, establish software engineering standards, and make informed decisions balancing system reliability, performance, and cost efficiency
- Develop, deploy, and maintain backend services using Python or Java to deliver property, listing, and recommendation data through scalable APIs and microservices
- Build high-throughput data processing workflows using Spark or Flink on EMR or comparable platforms, coordinated by Airflow and deployed on Kubernetes when appropriate
- Promote strong observability practices including metrics collection, distributed tracing, and centralized logging, along with operational rigor through alerting, runbooks, SLOs, and on-call support
- Develop and manage large-scale data pipelines that handle evolving schemas and process both real-time streams and batch loads from MLS and external sources
- Integrate data quality controls, lineage tracking, and governance policies into the platform foundation to serve analytics, machine learning, and customer-facing features
- Work with analytics and data science teams to improve data accessibility through semantic modeling, documentation, and self-service tools
- Partner with ML and AI engineers to build and scale intelligent agents that automate MLS feed integration and resolve listing inconsistencies
- Use frameworks like PydanticAI, LangChain, or equivalent to embed large language model agents into data and service architectures
- Establish evaluation frameworks, logging standards, and feedback mechanisms to ensure continuous improvement of AI agents and data products
- Collaborate with Product, Engineering, and Operations teams to define the strategic direction of the data platform, MLS functionality, and AI-driven user experiences
- Convert unclear business and customer needs into actionable technical roadmaps and incremental delivery plans
- Guide and support fellow engineers through mentorship, code reviews, pair programming, and architectural guidance to strengthen team-wide technical decision-making
Work Arrangement
Remote (Country)
