Director of Engineering (Data)

Join BuildOps, a company at the forefront of the AI revolution in the construction industry. As the Director of Engineering, Data, you will lead our unified Data & AI engineering function, owning the platforms, pipelines, quality standards, and operating discipline that power our AI and ML products. You will help define BuildOps' data strategy over the next three to five years and shape how we build data-powered products. This is a foundational leadership role for someone who can think long-term about architecture while staying close to the work.

- Diriger et superviser la fonction d'ingénierie des données et de l'IA, en possédant les plateformes, les pipelines, les normes de qualité et la discipline opérationnelle. - Définir la stratégie de données de BuildOps pour les trois à cinq prochaines années et façonner la manière dont l'entreprise construit des produits alimentés par les données. - Construire des capacités d'intégration des données des clients et des partenaires qui permettent d'intégrer rapidement et de manière fiable les données dans BuildOps.

- Both hands on and strategic, willing to write code, debug issues, and carry operational responsibility when needed while maintaining a clear multi year vision - A pragmatic approach to governance, with security and compliance built in, documentation and contracts that stay current, and access models that balance control with self service - Strong architectural judgment, including when to build, when to buy, how to design for change, and how to sequence investments behind validated needs instead of overbuilding - Early stage or scale up experience in Series B through D companies is a plus, and experience in vertical SaaS or construction tech is helpful but not required - You have seen this stage of growth before and know what good looks like in a high growth data organization - Direct experience building data systems that support production ML use cases, not just analytics, with an understanding of feature stores, training data, real time inference, and the tradeoffs involved in building versus buying ML platform capabilities - Deep hands on experience with modern data platforms, including a cloud warehouse or lakehouse such as Snowflake or Databricks, dbt and ELT patterns, orchestration tools like Airflow, and streaming technologies such as Kafka or Kinesis, with AWS experience strongly preferred - You know how to influence across Product, Engineering, GTM, Finance, and the executive team, build trust, communicate clearly, and push back when priorities are misaligned - A builder who is comfortable with ambiguity and greenfield work, and can move quickly, ship an MVP, and still make sound decisions for long term scale - Able to translate technical decisions into business impact and business goals into technical direction - Experience delivering customer facing data products such as reporting, analytics, or APIs in B2B SaaS environments where performance, reliability, and trust matter - A track record of operational excellence in data, including monitoring, SLAs, incident response, and data quality practices applied with the same rigor as customer facing systems - 10+ years in data or software engineering, including experience building and scaling teams of 10+ engineers, with a track record of hiring well, growing leaders, and leading through managers as well as individual contributors