Staff Software Engineer, Data Platform

🕒 il y a 5 mois

🇺🇸 États-Unis – Télétravail

💵 $200 000 - $230 000 / an

⏰ Temps Plein

🔴 Expert

🧑‍💻 Développeur Full-Stack

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

Postuler Maintenant

Trouver des Emplois à Distance Similaires

Recevoir des emails pour des emplois à distance

📊 Vérifiez votre score de CV pour ce poste

Améliorez vos chances d'obtenir un entretien en vérifiant votre score de CV avant de postuler.

Télécharger le CV

Luxury Presence

Site WebLinkedInTous les Emplois

201 - 500 employés

🏠 Immobilier

Real Estate • Marketing

Luxury Presence est une entreprise spécialisée dans la fourniture de solutions numériques de premier ordre aux professionnels de l'immobilier. Elle offre des conceptions de sites web immobiliers primées et des solutions de marketing expertises visant à aider les agents, les équipes et les agences à développer leurs activités et à construire leur marque. Leur plateforme inclut des outils pour la recherche de biens par IDX, des sites web immobiliers, des rapports d’estimation numérique (CMA), des services de référencement (SEO), du marketing de contenu, de la publicité et la gestion des réseaux sociaux. Adoubée par plus de 20 des 100 meilleurs agents du Wall Street Journal, Luxury Presence permet aux professionnels de l'immobilier d'attirer plus d'affaires grâce à des expériences numériques aussi belles que fonctionnelles.

Description

• 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 • 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

🎯 Exigences

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