VP Data Engineering

Consumer Edge is a company focused on redefining consumer data analytics, providing insights into consumer spending for investment firms and brands. They are seeking a seasoned VP of Data Engineering to lead their data engineering team, ensuring the reliability and scalability of their data infrastructure while managing multiple teams and collaborating across departments.

Responsibilities:

• 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
• 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
• 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

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
• 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
• Experience in fintech, alternative data, financial data, or another data-as-a-product environment strongly preferred
• 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