Senior Engineering Manager, Data

Built is looking for a Senior Engineering Manager, Data to lead our growing data organization. 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 team 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. If you enjoy building platforms, raising engineering maturity, and scaling both teams and systems, this role sits at that intersection.

Challenge

Real estate finance data is complex. It is high-volume, multi-source, time-sensitive, and often messy. 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

Responsibilities

Lead and grow the team

• 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

Own the data platform strategy

• 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

Deliver high-quality systems

• 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

Partner cross-functionally

• 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

Be a hands-on player-coach

• 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

Qualifications

Required

• 5+ years of experience in an engineering management role leading teams in a fast-paced, high-growth environment
• Direct experience leading a data engineering or data platform team specifically, not just general engineering management
• 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
• Travel Requirement: This role should expect to travel approximately five to six times per year, including two company-wide Connect Weeks and additional PD&E leadership travel weeks to Nashville, TN or another designated location. Exact cadence varies based on business needs and role responsibilities.

Preferred

• Experience with streaming data patterns and event-driven architectures using Kafka
• Experience operating production systems in AWS and partnering closely with platform and SRE teams
• Exposure to AI/ML or data science workflows: you'll oversee a staff engineer focused on AI/ML, but hands-on AI/ML experience is a plus, not a requirement
• Comfort working in TypeScript and Python ecosystems for data-adjacent services and tooling
• Familiarity with data quality testing, lineage, observability, and access control patterns