# Staff Data Engineer - US (Remote)

**Company:** [Luxury Presence](https://hotfix.jobs/companies/luxury-presence)
**Location:** Remote
**Role:** Data Engineering
**Salary:** $200k – $230k/yr
**Experience:** 10+ years
**Skills:** Python, Java, Apache Kafka, Spark, Flink, Airflow, Kubernetes, SQL, AWS, Emr, Pydanticai, LangChain, LangGraph, Rest, GraphQL
**Posted:** 2025-12-18

> Staff engineer builds and owns scalable data pipelines, backend services, and AI agents for MLS/property data platform, powering search, recommendations, and automation. Requires 10+ years experience with distributed systems, Python/Java, Kafka, Spark/Flink, and cloud infrastructure.

## Job Description

## What You’ll Do

### Technical leadership & architecture
- 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

### Backend, data & platform engineering
- 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

### Streaming & batch data pipelines
- 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)

### AI agents & data products
- 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

### Cross-functional impact & mentorship
- 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

## What You’ll Bring

### Experience & scope
- **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

### Core technical skills
- 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

### AI agent experience
- 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

### Leadership & collaboration
- 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

## Nice to Have
- 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

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