Senior Data Engineering Manager, Built Technologies

• If you enjoy building platforms, raising engineering maturity, and scaling both teams and systems, this role sits at that intersection
• 5+ years of experience in an engineering management role leading teams in a fast-paced, high-growth environment
• Proven ability to scale teams and systems through hiring, process, architecture, and delivery
• Excellent communication and collaboration skills across technical and non-technical stakeholders
• Passion for fostering a culture of innovation, learning, and continuous improvement
• Player-coach mindset with prior experience as an individual contributor
• Hands-on familiarity with Snowflake, DBT, or Sigma (deep experience in at least one)
• Experience building modern data platforms, including ELT, modeling layers, governance, and self-serve analytics

Desirable

• Built Technologies is looking for a Senior Engineering Manager to lead our growing Data Engineering team
• In this role, you will guide a high-impact group of engineers building the industry’s first holistic real estate data engine that powers analytics, decisioning, workflow automation, and new product capabilities across our platform
• You will lead the Data Engineering organization responsible for building and operating foundational data capabilities across Built, including ingestion, modeling, governance, reliability, and self-serve analytics
• Your team will enable product teams and business stakeholders to confidently use data to drive outcomes, while maintaining high standards for security, quality, and performance
• This is a hands-on, player-coach leader who can set technical direction, grow talent, and deliver durable systems at scale, while partnering closely with Product, Analytics, and Engineering peers
• This is an opportunity to lead a team building core infrastructure that shapes the company’s future
• You will influence how Built captures, normalizes, and activates the most critical data in our industry, with autonomy, executive visibility, and room to innovate
• The challenge is to build a data engine that is:
• Trusted: accurate, governed, explainable
• Fast: optimized performance and cost, low latency where it matters
• Composable: clean models that scale with new products
• Self-serve: enables Product, Analytics, and GTM teams
• Reliable: observable, resilient, and operationally excellent
• Coach engineers through clear expectations, feedback, and career development
• Hire and retain top talent and build a high-performance, inclusive culture
• Establish strong delivery and operational rituals, including planning, retrospectives, and incident reviews
• Define and evolve the architecture for ingestion, transformation, orchestration, governance, and data products
• Drive a roadmap that balances foundational platform investments with product delivery needs
• Champion best practices, including dbt patterns, data contracts, testing, and documentation
• Ensure pipelines and models are accurate, observable, secure, and scalable
• Improve reliability through alerting, SLAs and SLOs, runbooks, and root-cause analysis
• Partner with platform engineering on deployment patterns, cost optimization, and environment strategy
• Collaborate with Product, Analytics, Security, and Engineering leaders to ensure data enables customer and business outcomes
• Communicate clearly with stakeholders on tradeoffs, risks, and timelines
• Influence the broader organization on data quality, trust, and accountability
• Stay close to the work through architecture reviews, pairing, design docs, and occasional implementation
• Bring strong judgment to tooling and build-versus-buy decisions across Snowflake, DBT, and Sigma