Head of Data Platform Engineering, MD
State Street View all jobs
- Burlington, ON
- $170,000-267,500 per year
- Permanent
- Full-time
- Define and execute the long‑term vision and architecture for a unified, cloud‑native data platform supporting data ingestion, transformation, storage, governance, and access at scale.
- Lead the engineering teams building core components of the data platform: data lakehouse, real‑time streaming, metadata catalog, observability framework, and security layers.
- Drive selection and adoption of technologies (e.g., Snowflake, Databricks, BigQuery, Kafka, Airflow, dbt, Delta Lake, Iceberg, or similar) aligned with business requirements and cloud‑native best practices.
- Establish foundations for high‑quality data engineering: CI/CD pipelines, IaC (Terraform, CloudFormation), automated testing, schema versioning, and data quality monitoring.
- Partner with Product, Analytics, and ML teams to define data contracts, SLAs, and governance standards that ensure reliability, consistency, and compliance.
- Build, mentor, and scale a multi‑disciplinary engineering organization — attracting top data engineers, infrastructure specialists, and SREs.
- Represent the data platform strategy to executive leadership, ensuring alignment with enterprise technology and business goals.
- 20+ years of software engineering experience, including at least 10 years in data infrastructure or platform leadership roles.
- Proven record of leading large‑scale cloud migrations or greenfield cloud‑native data platform builds.
- Deep understanding of distributed data systems, including storage (object stores, data warehouses, lakehouses), compute (Spark, Flink, Presto), and event streaming (Kafka, Kinesis, Pub/Sub).
- Hands‑on background in modern DevOps and DataOps practices: CI/CD, container orchestration (Kubernetes), infrastructure automation, and observability.
- Familiarity with security and compliance frameworks (GDPR, SOC 2, HIPAA, etc.).
- Strong people‑leadership skills: team building, coaching, and creating high‑performance cultures across geographies.
- Experience partnering with analytics, ML, and enterprise application teams to deliver end‑to‑end data solutions.
- Exceptional communication skills with the ability to articulate complex technical concepts to executives.
- Prior experience architecting platforms using AWS (Glue, S3, EMR, Redshift) or Azure Data Services (Synapse, Data Factory, ADLS Gen2) or GCP (BigQuery, Dataflow, Pub/Sub).
- Familiarity with modern governance frameworks (Unity Catalog, Collibra, Alation).
- Exposure to AI/ML enablement (feature stores, model registries, data versioning).
- Engineering degree in Computer Science, Engineering, or related field.
- Delivery of a scalable, reliable, and secure data platform used by analytics, ML, and operational stakeholders across the company.
- Reduction in data latency, increase in data quality, and measurable platform adoption.
- Growth and retention of high‑performing engineering talent.
- Clear roadmap execution, cost efficiency, and alignment with business data initiatives.