Director of Data Engineering
| Company | Procore Technologies |
|---|---|
| Location | Austin, TX, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Expert or higher |
Requirements
- 12+ years of experience in Analytics, building and operating cloud-based, highly available, and scalable online serving or streaming systems utilizing large, diverse data sets in production.
- Experience scaling and managing 20+ person teams and in managing managers.
- Experience leading in a highly cross-functional environment, likely collaborating closely with Engineering, Product Management, and/or Data Science.
- Communication and leadership experience, with experience initiating and driving large scale strategic initiatives.
- Experience in SQL, Python, Java or similar languages, and data technologies like Databricks, PostgreSQL, GraphDB, NoSQL DB, Mongo, Cassandra, Elastic Search etc.
- Experience working in a on-shore/offshore operating model across multiple time zones.
- Strength in the majority of commonly used data technologies and languages such as Python, Java or Scala, Kafka, Spark, Airflow, Kubernetes, Docker, Argo, Jenkins or similar.
- Strong interpersonal skills with the ability to manage ambiguity and conflicts both inside and outside the team.
Responsibilities
- Proactively formulate and implement the vision for the foundation for Procore’s reporting, analytics, and predictive insight, and GenAI product features such as Copilot Q/A through reporting agent.
- Drive technical and process innovation to increase the maturity and capability of Infrastructure data engineering team and practices.
- Establish the processes needed to achieve operational excellence in data fidelity, data privacy, system reliability, and enabling rapid experimentation and data-informed decisions.
- Manage and grow a high-quality data engineering team.
- Partner with cross-functional teams of Data Scientists, Software Engineers, Product Managers, Researchers, and other partner groups to understand their data needs and deliver on them.
- Drive the design, building, and launching of new data models and data pipelines in production.
- Define and manage SLAs for all data sets and processes running in production.
- Drive data quality across the product vertical and related business areas.
Preferred Qualifications
- Experience In AI and the ML lifecycle is a plus.