VP Data Engineering

Role Summary 

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.