Data Engineer
Basic Qualifications Bachelor's degree in Computer Science, Engineering, MIS, or related field preferred. 12+ years of experience in data engineering or software engineering, with demonstrated experience architecting data platforms and
Basic Qualifications
Bachelor's degree in Computer Science, Engineering, MIS, or related field preferred.
12+ years of experience in data engineering or software engineering, with demonstrated experience architecting data platforms and pipelines at scale.
Expert-level SQL and strong proficiency in Python (Scala or Java a plus) for large-scale data processing and transformation.
Deep experience with cloud data platforms (e.g., Databricks, Snowflake, Synapse, BigQuery, Redshift) and cloud-native architecture patterns.
Deep understanding of distributed systems, data modeling (dimensional, data vault, lakehouse), and ETL/ELT architecture.
Hands-on experience designing and implementing Master Data Management (MDM) solutions, including entity resolution, match/merge, golden records, and reference/hierarchy management (e.g., Informatica, Reltio, Profisee, or similar).
Hands-on experience building or integrating agentic AI systems, LLM-powered applications, RAG pipelines, or AI agent orchestration frameworks (e.g., LangChain, AutoGen, Semantic Kernel, MCP).
Experience building backend data services and APIs (REST/GraphQL), with comfort working across the full stack.
Strong background with both relational (SQL) and NoSQL data stores, plus data lake/lakehouse formats (Delta, Iceberg, Parquet).
Deep understanding of CI/CD pipelines, infrastructure as code, and DevOps/DataOps practices.
Proven track record of leading large-scale technical initiatives across multiple teams.
Demonstrated ability to mentor engineers and influence technical direction without direct reporting authority.
Preferred Qualifications
Experience with data governance, lineage, and cataloging tools (e.g., Unity Catalog, Microsoft Purview, Collibra, Alation).
Experience designing multi-agent systems, tool-calling architectures, or retrieval-augmented generation (RAG) pipelines.
Experience with event-driven architectures and streaming/real-time data processing (e.g., Kafka, Event Hubs, Kinesis, Flink, Spark Structured Streaming).
Experience building the data layer for ML/AI, including feature stores, vector databases, embeddings, and ML/LLMOps.
Familiarity with containerization and orchestration (Docker, Kubernetes) and workflow orchestration (Airflow, Dagster, dbt).
Prior experience in commercial real estate, fintech, or operations/transaction systems.
Track record of speaking, writing, or open-source contributions that demonstrate technical thought leadership, especially in applied AI or data.
Why Join Us?
Shape the technical direction of business-critical data platforms at enterprise scale, including master data management and next-generation agentic AI initiatives.
Be part of a high-impact team where ownership, innovation, and technical excellence drive success.
Posted July 31, 2026