Data Engineering Lead
About The Position
QXO is North America’s largest distributor and installer of insulation; second-largest distributor of roofing products; second-largest publicly traded distributor of lumber and building materials; and largest distributor of waterproofing products. QXO is the fastest growing company in the $800 billion building products distribution industry and plans to become the tech-enabled leader by delivering best-in-class customer satisfaction and outsized returns for its shareholders. The company is targeting $50 billion in annual revenue within the next decade through accretive acquisitions and organic growth. Visit QXO.com for more information. We are looking for a Data Engineering Lead to design, build, and operate 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. You 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
- Python, SQL, Apache Spark (PySpark)
- BigQuery, Google Cloud Storage (GCS), Dataflow, Pub/Sub, Cloud Composer (Airflow)
- dbt, analytics‑ready and semantic data modeling
- OpenMetadata, OpenLineage, data quality testing, data observability
- Git, CI/CD for data pipelines, Infrastructure‑as‑Code (Terraform preferred), Docker fundamentals
- 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
- 401(k) with employer match
- Medical, dental, and vision insurance
- PTO, company holidays, and parental leave
- 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
