Senior Staff Data Engineer — Hybrid, AI-Driven Platforms

Newmark

Dallas (TX)

Hybrid

USD 190,000 - 250,000

Full time

14 days+

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Job summary

Newmark seeks a senior data platform architect to own the end-to-end data architecture for enterprise-scale initiatives. You will drive ingestion, transformation, and serving layers while leading MDM and AI-enabled workflows across cross-functional teams.

The role emphasizes designing scalable data pipelines, cloud-native architectures, and governance frameworks, with mentoring responsibilities and a focus on AI-enabled data capabilities. Hybrid work and competitive compensation are offered.

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 (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.

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 MDM, including golden records, entity resolution, data domains, reference and hierarchy management, and stewardship, to create trusted data.
Architect and integrate agentic AI and LLM-driven workflows 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 expose curated datasets to applications, analytics, and BI consumers.
Set engineering standards and best practices for data quality, modeling, testing, observability, and deployment, including AI tools usage.
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 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.