Data Platform Engineering Lead (Hands-On)

rbc; Toronto; On-site; CAD 150,000 - 190,000; Full time

Benefits offered by this job

Total rewards program

Professional development

Flexible work options

Stock opportunities where applicable

Job summary

RBC is seeking a hands-on Senior Manager, Engineering & Build to lead a high-impact data platform team in Toronto. You will mentor engineers, drive delivery excellence, and contribute ~50% hands-on to design cloud-native, governed data systems in AML.

The role blends leadership with technical execution across Snowflake, Spark, dbt, Airflow, Python/Scala/Java, and Kubernetes/OpenShift, delivering scalable pipelines and reliable data foundations for analytics and controls.

Qualifications

• 10+ years in engineering delivering production pipelines and distributed systems at scale.
• 2-4+ years leading engineers with hands-on leadership.
• Snowflake: Hands-on with Streams, Tasks, Snowpark, Snowpipe; strong SQL and warehouse design.
• Distributed processing with Spark (PySpark/Scala) for large-scale batch processing.
• Data modeling and medallion architecture patterns (Bronze/Silver/Gold).
• Orchestration and automation: Airflow; CI/CD for data workflows.

Responsibilities

• Lead, mentor, and grow a high-performing team with coaching and goal setting.
• Design and build batch/incremental pipelines across Bronze/Silver/Gold with Snowflake, Spark, dbt.
• Architect for reliability, scalability, and cost efficiency on cloud platforms.
• Containerize jobs/services and run on Kubernetes/OpenShift with IaC and secure secrets.
• Own CI/CD for data artifacts, environment parity, and data contracts.

Skills

Data engineering leadership

Snowflake

Spark

dbt

Airflow

Python/Scala/Java

Cloud platforms

Kubernetes/OpenShift

Data governance

CI/CD

Tools

Snowflake

Airflow

dbt

Kubernetes/OpenShift

Spark

Python/Scala/Java

Job description

RBC is seeking a hands-on Senior Manager, Engineering & Build to lead a high-impact data platform team in Toronto. You will mentor engineers, drive delivery excellence, and contribute ~50% hands-on to design cloud-native, governed data systems in AML.

The role blends leadership with technical execution across Snowflake, Spark, dbt, Airflow, Python/Scala/Java, and Kubernetes/OpenShift, delivering scalable pipelines and reliable data foundations for analytics and controls.