Staff Platform Engineer defining cloud and DevSecOps strategy for Robots & Pencils’ enterprise AI systems. Leading Kubernetes, AI/ML infrastructure, migrations, reliability, security, and platform standards remotely in Canada.
About the role
- Senior Data Platform Engineer optimizing data processes for a Montreal IT consulting firm. Involving governance, ingestion pipelines, and scalable architecture in data management.
Responsibilities
- Deliver curated, reusable datasets for BI, analytics, and ML.
- Build and run ingestion, transformation, and serving pipelines (batch and streaming).
- Implement governance: ownership, PII handling, RBAC, retention, audit and compliance.
- Set up and monitor data quality and observability (checks, alerts, incident handling).
- Design and own scalable data platform architecture (lake/Lakehouse/warehouse).
- Define and track dataset SLOs/SLAs (freshness, completeness, latency).
- Define and enforce data standards: modeling, layering, naming, metadata, contracts.
Requirements
- 6+ years in data architecture, data engineering, and DataOps (production environments)
- Strong SQL experience
- Strong Python experience
- Experience building and maintaining production data pipelines
- Experience designing and operating cloud data platforms (AWS and/or Azure or equivalent)
- Experience with data lakes, Lakehouse, and/or data warehouses
- Experience with data modeling and layering
- Experience with data contracts and schema management
- Experience with data governance (ownership, PII handling, RBAC, retention, auditability)
- Experience with data quality controls and observability
- Experience with metadata and data lineage
- Experience with CI/CD practices for data systems
- Experience with infrastructure as code (e.g., Terraform or equivalent)
- Experience with batch and streaming data processing
- Experience with at least one orchestration tool (Airflow, Dagster, Prefect, or similar)
- Experience with at least one transformation tool (dbt or equivalent)
- Experience with at least one streaming system (Kafka, Kinesis, Event Hubs, Flink, or Spark Streaming)
- Experience with Lakehouse table formats (Delta, Iceberg, or Hudi)
- Experience with catalog and governance tools (Purview, Collibra, DataHub, Unity Catalog, or Alation)
- Experience with BI tools (Power BI, Tableau, or Looker)
- Experience with data quality/observability tools (Great Expectations, Soda, or equivalent)
- Familiarity with Kubernetes and containerized workloads
Benefits
- Flexible work arrangements
- Professional development opportunities
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