VP Data Engineering
About The Position
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.
Requirements
- 10+ years in data engineering, with at least 3 years in a leadership or management role (managing managers and teams)
- Proven experience building and operating large-scale data pipelines on Google Cloud Platform
- Experience in fintech, alternative data, financial data, or another data-as-a-product environment strongly preferred
- Track record of delivering high-quality data infrastructure in a fast-moving, commercially sensitive context
- Deep expertise across the GCP data stack: BigQuery, Dataflow, Pub/Sub, GCS, Cloud Composer, and related services
- Hands-on experience with dbt (Core or Cloud) for scalable transformation layer design, including modeling patterns, testing frameworks, and documentation standards
- Strong understanding of data pipeline design, ELT/ETL patterns, data modelling, and workflow orchestration
- Solid grasp of data governance, quality frameworks, and security best practices
- Familiarity with the unique challenges of alternative data — diverse formats, inconsistent schemas, high ingestion volumes, and strict data provenance requirements
- Practical experience with infrastructure-as-code tooling (Terraform and/or Pulumi) for provisioning and managing cloud resources; able to set IaC standards and review infrastructure changes with the same rigour applied to application code
- Strong proficiency in Python and SQL; comfortable reviewing code and setting engineering standards across the team.
- Excellent communicator with the ability to engage engineers, data scientists, and commercial stakeholders alike
- Strong hiring instincts and a genuine passion for developing people
- Pragmatic and decisive — able to balance technical rigour with commercial urgency
- High ownership mindset; comfortable operating with autonomy in a high-stakes environment
Nice To Haves
- Experience supporting data science or ML teams with feature engineering infrastructure
- Familiarity with data licensing, provenance tracking, or data vendor management
- Experience with data mesh or data-as-a-product organisational models
- Open source contributions or published technical work
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: cost optimisation, observability, incident response, and disaster recovery
- Ensure data security, access controls, and compliance standards appropriate for regulated financial services clients
- Strategic & Cross-functional
- Translate business priorities and client requirements into a clear, deliverable technical roadmap
- Partner with data operations, data science, and product teams to accelerate dataset onboarding and expand platform capabilities
- Represent data engineering at the leadership level — contributing to company strategy and advocating for data quality as a core business value
Benefits
- We offer a competitive salary, an extensive benefits package including 401(k) match, paid parental leave, flexible and generous time off, work-from-home flexibility, and a vibrant work environment conducive to professional growth and innovation.
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What This Job Offers
Job Type
Full-time
Career Level
Executive
Education Level
No Education Listed
