Staff Software Engineer

RBGlobal•Burnaby, BC

54d•CA$106,090 - CA$132,615•Hybrid

About The Position

We are seeking a highly skilled Staff Software Engineer to join our Data Engineering team. In this senior technical leadership role, you will drive the architecture, design, and evolution of our enterprise data platform. You will set technical direction, mentor engineers, and champion engineering excellence across the team. Collaborating closely with data architects, engineering managers, business analysts, analytics engineers, data scientists, and product teams, you'll define and deliver the next generation of data products and platform capabilities at scale. The Data Engineering team designs, builds, and operates scalable data pipelines that power analytics, reporting, and machine learning across the organization. Our platform ingests data from multiple transactional systems, streaming platforms, and third-party integrations into our cloud data warehouse on AWS. We are also investing in AI-driven development practices to accelerate delivery and improve data quality across the platform. Staff Engineers cultivate a healthy and respectful environment with a passion for driving innovation and excellence.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field.
10+ years of experience in data engineering or software engineering, with at least 3+ years operating at a senior/staff level.
Strong proficiency in Python (required) and SQL, with a solid foundation in object-oriented programming; experience with additional languages such as Java or Scala is a plus.
Expert-level knowledge of cloud data warehouses (e.g., Snowflake, BigQuery) — Snowflake experience highly preferred, including semantic views, access policies, and governance capabilities.
Proven track record of designing and scaling data platforms in production environments.
Deep experience with dbt for data transformation at scale.
Strong expertise in data modeling — dimensional modeling, star/snowflake schemas, data vault.
Extensive experience with workflow orchestration tools (Apache Airflow preferred).
Strong understanding of streaming technologies (Apache Kafka, Snowpipe) and event-driven architectures.
Experience with AWS services (S3, MWAA, Glue) in production data environments.
Proficiency with Infrastructure as Code (Terraform) and CI/CD tools (CircleCI, Harness, GitHub Actions).
Experience with Datadog or similar monitoring and observability platforms, including defining SLOs and alerting strategies.
Demonstrated ability to mentor engineers, lead design reviews, and influence technical direction without direct authority.
Strong communication and collaboration skills, with the ability to work effectively in a hybrid team environment.
Proficiency with Git/GitHub and modern development workflows.
Strong advocate for AI-driven development tools and methodologies.
Familiarity with BI tools such as Power BI, Tableau, or Looker/LookML.
Experience with data governance and cataloging tools (Horizon Catalog or Select Star).
Knowledge of HashiCorp Vault or secrets management solutions.
Experience with data quality/testing frameworks.
Experience contributing to or leading technical RFCs and architecture decision records (ADRs).

Responsibilities

Define and drive the technical vision for the enterprise data platform, making key architecture and technology decisions that impact the entire data organization.
Lead the design of scalable, resilient ETL/ELT data pipelines using dbt, Python, and SQL, establishing patterns and best practices for the team to follow.
Architect and optimize data models and transformations within cloud data warehouses (Snowflake, BigQuery), ensuring performance, cost-efficiency, and maintainability.
Architect and standardize the use of Snowflake semantic views to establish a governed semantic layer, ensuring consistent metric definitions across Power BI, Tableau, and Looker.
Own the design and evolution of workflow orchestration using Apache Airflow (AWS MWAA), driving improvements in reliability and developer experience.
Lead the strategy for real-time streaming ingestion using Kafka and Snowpipe Streaming, evaluating trade-offs and guiding implementation.
Drive CI/CD strategy and Infrastructure as Code practices using Terraform, CircleCI, Harness, and GitHub Actions — raising the bar for deployment reliability and velocity.
Champion and advance data modeling standards (dimensional modeling, star/snowflake schemas, data vault) across the platform.
Define and implement observability strategy using Datadog, including SLOs, alerting, and dashboarding for pipeline and platform health.
Lead the adoption of AI-driven development tools and practices across the team, identifying high-impact opportunities to improve engineering productivity, code quality, and pipeline reliability.
Define and operationalize data governance metrics — including data quality scores, lineage coverage, ownership accountability, and policy adherence — to measure and continuously improve platform trustworthiness.
Lead investigation and resolution of complex data issues by analyzing end-to-end data lineage, identifying root causes, and driving systemic fixes to ensure the accuracy and trustworthiness of critical business metrics.
Establish and enforce data quality, governance, and lineage standards across the platform.
Mentor and support the growth of junior, intermediate, and senior engineers through knowledge sharing, pairing, technical guidance, and feedback.
Collaborate with data architects, business analysts, engineering leadership, and cross-functional stakeholders to align technical strategy with business objectives.
Partner with Engineering Managers to evaluate team performance, provide input for annual reviews, and participate in hiring initiatives.
Support and improve data consumption layers including Power BI, Tableau, and Looker.
Drive engineering best practices including code reviews, technical documentation, incident response processes, and post-mortems.
Collaborate with other Staff and Sr. Staff Engineers to create a community of practice and an overall organizational technical strategy for business success.
Influence and publish within the organization and industry to effect positive change in data engineering strategy and advance the craft of data platform development worldwide.
Perform other duties and responsibilities as assigned in support of organizational priorities.