Lead Data Engineer

What is the opportunity?
Are you a hands-on data platform engineer who thrives on building cloud-native, high-scale data platforms and enabling teams on top of them? Come join us!

Job Description
What is the opportunity?
Are you a hands-on data platform engineer who thrives on building cloud-native, high-scale data platforms and enabling teams on top of them? Come join us!

Global Functions Technology (GFT) partners across RBC to deliver transformative platforms and solutions. In Anti-Money Laundering (AML), we are building a new Data Foundation Hub to ingest enterprise data and power analytics and controls using a medallion architecture. As a Lead Data Platform Engineer, you will be a senior individual contributor and technical lead, owning the design and build of our AWS-based data platform and mentoring other engineers.

You will work 70-80% hands-on across AWS (EKS, S3, RDS, EMR, Glue, Airflow), Snowflake, Spark, and dbt to deliver cloud-native, governed, and reliable data systems.

What will you do?

Technical leadership and platform ownership

Lead the technical direction for the AML Data Foundation Hub on AWS.

Mentor and coach engineers (tech design reviews, pair programming, standards), influencing quality and delivery

Cloud-native data platform on AWS (hands-on)

Design and build secure, scalable data platforms using AWS S3, Glue, EMR, RDS, and EKS

Define patterns for data lake and warehouse integration (e.g., S3 + Snowflake) including partitioning, storage classes, encryption, and cost optimization.

Implement Infrastructure-as-Code (e.g., Cloud Formation/Terraform) for repeatable environments, networking, IAM roles/policies, and security baselines.

Data engineering and architecture (medallion)

Design and build batch and incremental pipelines across Bronze/Silver/Gold layers using Snowflake (Streams, Tasks, Snowpark), Spark on EMR, and dbt

Implement schema evolution, SCD/CDC, partitioning, and performance tuning across both compute and storage (S3, EMR, Snowflake, RDS).

Ingestion, orchestration, and observability

Engineer resilient, observable ingestion patterns into S3/Snowflake/RDS.

Orchestrate pipelines using Airflow (or equivalent) and/or AWS-native services (e.g., event triggers), enforcing SLAs, retries, idempotency, and alerting

Build operational dashboards and alerts for pipeline health, platform capacity, and cost.

Reliability, DR, and security

Design for high availability, resiliency, and disaster recovery (multi-AZ/multi-region backup/restore, RPO/RTO-aware architectures).

Implement secrets management, encryption, IAM least-privilege, and network security in partnership with Security and Platform/SRE.

Participate in incident response and postmortems; drive root-cause fixes and hardening of the platform.

Dev Ops for data and platform enablement

Own CI/CD for data and platform components: code review, environment promotion, automated tests (unit, integration, data contract), and versioned artifacts.

Partner with Platform/SRE on SLIs/SLOs, capacity planning, and platform standardization across squads.

Cross-functional collaboration

Translate AML business and control objectives into technical roadmaps, platform capabilities, and reusable patterns

What do you need to succeed?
Must-have

Experience depth: 7+ years delivering production data pipelines and distributed systems at scale on cloud platforms; demonstrated ability to operate as a senior IC and technical lead influencing architecture and quality across a team.

AWS platform depth:
Hands-on with S3, Glue, EMR, EKS, and RDS; proficiency with IaC (Cloud Formation or Terraform), IAM least-privilege design, VPC/networking, and security baselines.

Snowflake expertise:
Hands-on with Streams, Tasks, Snowpark, and Snowpipe; strong SQL and warehouse design; performance optimization across compute and storage.

Distributed processing:
Production experience with Spark (PySpark/Scala) for large-scale batch processing, optimization, and tuning.

Data engineering and architecture:
Medallion architecture patterns (Bronze/Silver/Gold), schema evolution, SCD/CDC, partitioning, and end-to-end pipeline performance tuning.

Orchestration and automation:
Airflow (or equivalent) for DAGs, SLAs, retries, idempotency, and observability;
Git-based workflows and CI/CD for data pipelines (e.g., Git Hub Actions/Jenkins).

Reliability and security:
Designing for HA/DR (multi-AZ, backup/restore, RPO/RTO); encryption, secrets management, and network security in partnership with Platform/SRE.

Dev Ops for data:
Ownership of automated testing (unit, integration, data contract), environment promotion, and versioned artifacts.

Ways of working:
Strong ownership, structured problem-solving, and clear technical communication; experience with incident response and postmortems.

Nice-to-have

dbt proficiency:
Development, testing, documentation, and deployment of transformations with dbt.

Observability:
Metrics, tracing, and logging practices across data pipelines and platform components.

Security and privacy: OAuth2/OIDC, data…