VP Data Engineering

The Vice President of Data Engineering is responsible for defining, building, and scaling a modern, enterprise-wide data engineering capability within a federated operating model. This role will lead the design and delivery of robust, secure, and high-performing data pipelines, with a strong focus on AWS-native architectures and Snowflake-based data warehousing.

The VP of Data Engineering will establish best-in-class engineering practices, enabling domain-oriented data ownership while ensuring consistency through shared standards, governance, and platform capabilities. A critical aspect of the role is enabling the development of AI-ready data ecosystems, including knowledge graphs, ontologies, and semantically enriched datasets that support advanced analytics, machine learning, and AI-native applications.

Role Responsibilities

• Define and execute the enterprise data engineering strategy aligned to a federated (data mesh-style) operating model, balancing domain autonomy with centralized governance.
• Build, scale and lead a high-performing data engineering organization, including platform, enablement, and domain-aligned teams.
• Architect and oversee scalable, secure data platforms leveraging AWS services (e.g. S3, Glue, Lambda, EMR, Redshift), dbt and Snowflake.
• Establish best practices for data ingestion, transformation, orchestration, and serving (batch, streaming, and real-time patterns).
• Drive adoption of modern data engineering principles including DataOps, CI/CD, infrastructure-as-code, and automated testing frameworks.
• Define and enforce data governance standards, including data quality, lineage, cataloging, security, and compliance across federated domains.
• Enable self-service data capabilities through reusable data products, shared tooling, and developer platforms.
• Lead the design and implementation of AI-native data architectures, including feature stores, vector databases, and semantic layers.
• Champion the creation and integration of knowledge graphs and ontologies to enhance data discoverability, interoperability, and contextual understanding.
• Collaborate with senior stakeholders across engineering, product, analytics, and AI/ML teams to deliver business value through data.

Key Skills And Experience

• Proven experience leading large-scale data engineering organizations in complex, federated or matrixed environments.
• Deep expertise in AWS data ecosystem (S3, Glue, Lambda, Kinesis, EMR, IAM, Lake Formation) and cloud-native architecture patterns.
• Strong hands-on and architectural experience with Snowflake/dbt/Airflow, including performance optimization, data modelling, and cost management.
• Expertise in building scalable modern data platforms (data lakes, lakehouses, and data warehouses) enabling reliable real-time and batch analytics.
• Strong understanding of distributed data processing frameworks (e.g. Spark, Flink) and streaming technologies.
• Demonstrated implementation of DataOps practices, including CI/CD pipelines, observability, testing, and automated deployments.
• Experience designing and operationalizing data governance frameworks in a federated or data mesh environment with self-service and trusted data capabilities.
• Highly versed in delivering ML/AI-ready ecosystems (feature stores, semantic layers, graph databases) aligned with executive stakeholders to drive business impact.
• Practical experience with knowledge graphs, ontologies, semantic modelling (e.g. RDF, OWL), delivering faster insights.
• Strong leadership, stakeholder management, and communication skills, with the ability to influence at executive level and drive organizational change.

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