Staff Data Engineer at Newmark in Chicago, IL, United States

Responsibilities

• Own and drive the technical architecture for complex, cross-team data initiatives spanning ingestion, transformation, storage, and serving layers.
• Design, build, and maintain scalable, high-performance data pipelines and distributed data platforms in a cloud-native environment (Azure, AWS, or GCP).
• Architect and lead enterprise Master Data Management (MDM), including golden records, entity resolution, data domains, reference and hierarchy management, and stewardship, to create trusted, authoritative data across the business.
• Architect and integrate agentic AI and LLM-driven workflows (autonomous agents, RAG pipelines, AI copilots) into data platforms and pipelines to drive efficiency and new capabilities.
• Build and support the data foundations for machine learning and AI, including feature stores, vector stores, embeddings, and ML/LLMOps pipelines.
• Design and deliver backend data services and APIs (REST/GraphQL), and contribute across the stack to expose curated datasets to applications, analytics, and BI consumers.
• Set engineering standards and best practices for data quality, modeling, testing, observability, and deployment across the organization, including responsible use of AI-assisted development tools.
• Establish data governance, lineage, cataloging, and quality frameworks across the data estate.
• Lead technical design reviews and provide architectural guidance to multiple engineering and data teams.
• Partner with product, analytics, and engineering leadership to translate business strategy into scalable data roadmaps, including AI-driven capabilities.
• Identify and resolve systemic performance, reliability, and scalability issues across the data stack.
• Mentor and coach senior and mid-level engineers, raising the technical bar across the organization on data engineering, MDM, and AI practices.
• Drive adoption of modern frameworks, tools, and engineering practices, including agentic AI and LLM tooling, to improve delivery velocity and platform resilience.
• Maintain awareness of emerging technologies and industry trends, particularly in agentic AI, master data management, and modern data platforms, and assess their applicability to the business.

Requirements

• 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.
• 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.

Skills

Required

• Expert-level SQL
• Strong proficiency in Python
• Scala or Java (plus)
• Distributed systems
• ETL/ELT architecture
• Relational (SQL) and NoSQL data stores

Benefits

• 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.
• Competitive compensation, growth opportunities, and access to world-class engineering, data, and AI resources.
• Collaborative Culture: Join a high-caliber team with deep expertise across data engineering, cloud, MDM, agentic AI, and distributed systems.
• Growth & Learning: Access world-class learning resources and mentorship to advance your career.
• Work-Life Balance: Flexible working hours and hybrid options.
• Benefits: Comprehensive health, dental and vision insurance.
• If you're passionate about architecting scalable data platforms, building trusted master data and agentic AI-driven solutions, and shaping engineering culture, we'd love to hear from you!
• Apply now and help redefine the future of data and AI at scale!
• The expected base salary for this position ranges from $190,000 to $225,000 annually. The actual base salary will be determined on an individualized basis taking into account a wide range of factors including, but not limited to, relevant skills, experience, education, and, where applicable, licenses or certifications held. In addition to base salary and a competitive benefits package, this position may be eligible for additional types of compensation including discretionary bonuses and other short- and long-term incentives (e.g., deferred cash, equity, etc.).