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Director of Data Engineering & Platforms

Cotality

Dallas, Texas

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JOB DETAILS
SKILLS
Architectural Services, Artificial Intelligence (AI), Best Practices, Business Intelligence, Cloud Computing, Data Analysis, Data Management, Data Modeling, Data Quality, Data Warehousing, Database Extract Transform and Load (ETL), Ecosystems, Mentoring, Metadata, Performance Tuning/Optimization, Privacy Regulations, Product Engineering, Product Management, Python Programming/Scripting Language, Quality Monitoring, Real Estate, Snowflake Schema, Software Engineering, Standards Development, Strategic Planning, Team Lead/Manager, Technical Delivery, Technical Leadership
LOCATION
Dallas, Texas
POSTED
12 days ago
At Cotality, we are driven by a single mission-to make the property industry faster, smarter, and more people-centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.

Cotality is committed to cultivating a diverse and inclusive work culture that inspires innovation and bold thinking; it's a place where you can collaborate, feel valued, develop skills and directly impact the real estate economy. We know our people are our greatest asset. At Cotality, you can be yourself, lift people up and make an impact. By putting clients first and continuously innovating, we're working together to set the pace for unlocking new possibilities that better serve the property industry.

Job Description:

We are seeking a hybrid or remote Director of Data Engineering & Platforms to lead our data transformation initiatives and establish robust data architecture frameworks. This role reports to the AVP of Cloud Engineering and is responsible for designing, implementing, and maintaining our enterprise data ecosystem across bronze, silver, and gold data layers following Data Mesh and Data Vault methodologies.

This role sits at the intersection of data engineering and AI enablement, where the decisions made in the data layer directly shape the quality of what AI systems can deliver. The ideal candidate will drive technical leadership and strategic direction for our data platform while partnering with Product Management, Data Analytics, Data Systems, Cloud Engineering, and product teams to transform raw data into actionable business intelligence.

Key Responsibilities:

Strategic Leadership & Architecture
  • Lead the development and implementation of our data engineering strategy, architecture roadmap, and technical standards
  • Oversee the design and evolution of our data ecosystem utilizing Data Vault methodologies and Data Mesh principles
  • Establish governance and quality frameworks across Bronze (raw), Silver (transformed), and Gold (consumption-ready) data layers
  • Partner with Product Management to align data platform capabilities with business objectives and market demands
  • Drive the technical roadmap for data integration, transformation, and delivery systems, with explicit milestones for AI readiness

Technical Direction & Delivery
  • Provide technical leadership and oversight for the data engineering team, ensuring best practices in data pipelines, transformations, and delivery
  • Oversee the design and implementation of Snowflake data architecture including warehousing, marts, and access patterns optimized for both BI and AI workloads
  • Direct the development of robust ETL/ELT processes using Matillion, Python-based pipeline frameworks, and other modern data integration tools
  • Guide the implementation of data quality monitoring, lineage tracking, and metadata management
  • Establish standards for data modeling, transformation logic, and performance optimization

AI Data Infrastructure
  • Partner with AI/ML and product engineering teams to ensure the data layer supports LLM-powered applications reliably and at scale
  • Provide architectural direction for retrieval and grounding pipelines, including vector stores, embedding workflows, and hybrid search infrastructure
  • Define standards for data preparation for AI, covering metadata enrichment, context optimization, and semantic indexing
  • Build observability into AI data flows and monitor for drift and retrieval quality degradation
  • Guide the team's evaluation and adoption of emerging AI-native data tools, including vector databases and LLM orchestration frameworks

AI Governance & Risk
  • Establish governance frameworks for AI data use, including data lineage into models, PII controls upstream of LLM consumption, and output auditability
  • Define the organization's standards for acceptable AI data quality thresholds and remediation workflows
  • Partner with Security and Compliance to ensure AI data pipelines meet regulatory and privacy requirements

Team Leadership & Development
  • Build, mentor, and lead a high-performing data engineering team with strong core data engineering fundamentals and a growing fluency in modern AI infrastructure
  • Colla

About the Company

C

Cotality

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