Senior Engineering Manager, Data

Built Technologies•Nashville, TN

16h•$225,000 - $265,000•Hybrid

About The Position

Built is seeking a Senior Engineering Manager, Data to lead its data organization. This role involves guiding a team of engineers in building a comprehensive real estate data engine for analytics, decisioning, workflow automation, and new product features. The manager will oversee the data engineering team responsible for foundational data capabilities like ingestion, modeling, governance, reliability, and self-serve analytics. The goal is to empower product teams and business stakeholders to leverage data effectively while maintaining high standards for security, quality, and performance. This is a hands-on, player-coach leadership position requiring technical direction, talent development, and the ability to deliver scalable systems, in close collaboration with Product, Analytics, and Engineering peers. The role offers an opportunity to shape the company's future by influencing how Built captures, normalizes, and activates critical industry data, with autonomy, executive visibility, and room for innovation. It is ideal for individuals who enjoy building platforms, enhancing 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.
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, coaching engineers through clear expectations, feedback, and career development.
Hire and retain top talent, building a high-performance, inclusive culture.
Establish strong delivery and operational rituals, including planning, retrospectives, and incident reviews.
Own the data platform strategy, defining and evolving 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, ensuring 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 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.
Act as a hands-on player-coach, staying 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.