VP Data Engineering

Role Summary

The Vice President of Data Engineeringis responsible fordefining 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 datapipelines with a strong focus on AWS-native architectures and Snowflake-based data warehousing.

The VPof Data Engineeringwillestablishbest-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 isenablingthe 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

• Buildscaleand lead a high-performing data engineering organization including platform enablement and domain-aligned teams

• Architect and oversee scalable secure data platformsleveragingAWS services (e.g. S3 Glue Lambda EMR Redshift)dbtand Snowflake

• Establish best practices for data ingestion transformation orchestration and serving (batch streaming and real-time patterns)

• Drive adoption of modern data engineering principles includingDataOps CI/CD infrastructure-as-code and automated testing frameworks

• Define and enforce data governance standards including data quality lineagecataloging 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

• Deepexpertisein 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 datamodelling and cost management

• Expertisein buildingscalablemodern data platforms (data lakeslakehouses 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 ofDataOpspractices including CI/CD pipelines observability testing and automated deployments

• Experience designing and operationalizing data governance frameworks in a federated or data mesh environmentwithself-serviceand trusteddata capabilities

• Highly versed indelivering ML/AI-ready ecosystems (feature stores semantic layers graphdatabases)alignedwith executive stakeholderstodrivebusiness impact

• Practical experience with knowledge graphs ontologies semanticmodelling(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.