VP Data Engineering
Source: remoteok
Location: Remote (US-based; east coast preferred)
Company Overview
Join a dynamic team that's redefining consumer data analytics. We empower top investment firms and global consumer and corporate brands with cutting-edge insights into consumer spending, leveraging privacy-compliant data across geographies. Our real-time intelligence and merchant-level benchmarks give clients a competitive edge—and you'll be at the forefront of it all.
About the Role
We're looking for a seasoned VP of Data Engineering to lead our data engineering team and take ownership of the infrastructure that powers everything we do. Because data is our business, this role carries significant weight: the reliability, scalability, and quality of our data pipelines directly impacts our customers and our revenue.
You'll manage ~15 engineers across 3 data engineering teams, set technical direction across our GCP-based data platform, and work closely with data operations, product, and commercial teams to ensure we can continuously ingest, process, and deliver alternative datasets at scale — with the rigor that financial services clients demand.
Responsibilities
Team Leadership
- Lead, mentor, and grow a team of data engineers, building a culture of ownership, craft, and continuous improvement
- Own hiring, onboarding, and performance management for the data engineering function
- Act as a technical role model — setting high standards while remaining approachable and supportive
Data Platform & Infrastructure
- Own the architecture, reliability, and evolution of our GCP data platform — including BigQuery, Cloud Composer/Airflow, Dataflow, Pub/Sub, and GCS
- Design and maintain robust, scalable pipelines for ingesting, transforming, and serving diverse alternative datasets (web, CPG, transaction data, etc.)
- Drive infrastructure best practices, including monitoring, alerting, and incident response
- Collaborate with product and commercial teams to understand data needs and translate them into technical requirements
- Ensure data quality and compliance with privacy regulations
- Manage data platform costs and optimize resource utilization