Senior Engineering Manager, Data

Built Technologies•Nashville, TN

3d•Hybrid

About The Position

Built is looking for a Senior Engineering Manager, Data to lead their growing data organization. This role 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 the platform. The manager will lead the data engineering team responsible for building and operating foundational data capabilities, including ingestion, modeling, governance, reliability, and self-serve analytics. The 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. The role involves influencing how Built captures, normalizes, and activates critical industry data, with autonomy, executive visibility, and room to innovate. It is ideal for someone who enjoys building platforms, raising engineering maturity, and scaling both teams and systems.

Requirements

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

Nice To Haves

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

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