VP Data Engineering

Consumer EdgeNew Yorkfull-time
AI Summary: Lead a team of ~15 data engineers across 3 teams, set technical strategy for a GCP data platform, and coordinate between engineering, operations, product, and commercial stakeholders to ensure reliable data pipeline delivery for financial services clients.
Career-change fit:7/10
Project ManagementRemote
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Originally posted onremoteok on 3/27/2026
Full Description

This role is 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.

Role Summary

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 rigour that financial services clients demand.

Your Main 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.)