Data Engineering Lead
About The Position
QXO is a publicly traded company founded by Brad Jacobs, aiming to build the market-leading company in the building products distribution industry. Following its first acquisition of Beacon Building Products, QXO is focused on creating a customer-focused, tech-enabled, and innovation-driven business that will scale rapidly through accretive M&A, organic growth, and greenfield expansion. The company's strategy emphasizes delivering exceptional customer experiences, improving operational efficiency, and leveraging data, digital tools, and AI to modernize a historically under-digitized industry. The Data Engineering Lead will be responsible for designing, building, and operating scalable, cloud-native data platforms and data products on Google Cloud. This role combines hands-on engineering with technical leadership, setting standards and guiding best practices while partnering closely with analytics, product, and business teams to deliver trusted, analytics-ready data at scale. The lead will play a key role in shaping data architecture, improving data quality and reliability, and enabling BI, analytics, and AI use cases across the organization.
Requirements
- Programming & Processing: Python, SQL, Apache Spark (PySpark)
- GCP Data Stack: BigQuery, Google Cloud Storage (GCS), Dataflow, Pub/Sub, Cloud Composer (Airflow)
- Data Modeling & Analytics Engineering: dbt, analytics-ready and semantic data modeling
- Metadata, Lineage & Quality: OpenMetadata, OpenLineage, data quality testing, data observability
- DevOps & Platform Practices: Git, CI/CD for data pipelines, Infrastructure-as-Code (Terraform preferred), Docker fundamentals
- Security & Governance: Data access controls, data contracts, schema enforcement, governance-by-design
Nice To Haves
- Experience with real-time / streaming architectures
- Exposure to ML/AI data pipelines and feature engineering
- API-based and SaaS data integrations
- Familiarity with BI tools (e.g., Looker)
- Multi-cloud or hybrid data environments
Responsibilities
- Design, build, and maintain batch and streaming data pipelines on GCP
- Lead end-to-end data product engineering, from ingestion through transformation and consumption layers
- Define and enforce data engineering standards, patterns, and best practices
- Implement data quality, metadata, lineage, and observability as part of pipelines
- Own orchestration, CI/CD, and release processes for data workloads
- Partner with analytics, BI, ML, and business teams to deliver analytics-ready datasets
- Provide technical leadership and mentorship to data engineers across teams
Benefits
- Annual performance bonus
- Long term incentive (equity/stock)
- 401(k) with employer match
- Medical, dental, and vision insurance
- PTO, company holidays, and parental leave
- Paid Time Off/Paid Sick Leave: Applicants can expect to accrue 15 days of paid time off during their first year (4.62 hours for every 80 hours worked) and increased accruals after five years of service.
- Paid training and certifications
- Legal assistance and identity protection
- Pet insurance
- Employee assistance program (EAP)
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
