Staff Software Engineer, Data Platform - CANADA (Remote)

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 influencing architecture, standards, and technical direction. Strong programming skills in Python or Java. Experience building microservices and APIs (REST/GraphQL). 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. Experience with Airflow (or equivalent orchestration tools). Experience with 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. Experience building or integrating AI agents into production workflows. Familiarity with frameworks such as PydanticAI, LangGraph, Claude Code or similar. 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. Track record of mentoring other engineers. Strong communication skills.

Own end-to-end architecture for MLS and property data, including streaming and batch pipelines, microservices, storage, and APIs. Design and evolve event-driven, Kafka-based data flows. Drive technical design reviews and set engineering best practices. Design, build, and operate backend services (Python or Java) exposing data via APIs and microservices. Implement scalable data processing with Spark or Flink on EMR, orchestrated via Airflow and running on Kubernetes. Champion observability and operational excellence for data and backend services. Build and maintain high-volume, schema-evolving streaming and batch pipelines for MLS and third-party data ingestion and normalization. Ensure data quality, lineage, and governance are built into the platform. Partner with analytics engineering and data science to make data discoverable and usable. Collaborate with ML/AI engineers to design and scale AI agents for automation. Work with frameworks such as PydanticAI, LangChain, or similar to integrate LLM-based agents. Define and implement evaluation, logging, and feedback loops for AI agents and data products. Collaborate with Product, Engineering, and Operations to shape the data platform roadmap. Translate business problems into technical strategies and delivery plans. Mentor and unblock other engineers, and elevate technical decision-making.