Remote Knowledge Graph Software Engineer Jobs in Vancouver, BC

The Role
As a Senior Software Engineer, Big Data, you will own the design, delivery, and reliability of the core data systems powering our platform. You will strengthen our data platform capabilities as we continue scaling our systems and AI-driven initiatives, serving as a central force in managing our data warehouse and driving large-scale data initiatives like ML feature projection.

In this role, you will work across the full data lifecycle-building, optimizing, and scaling pipelines that power analytics, machine learning, and activation across billions of events and diverse data sources.

Location: Our Vancouver office will open on September 1 in Mount Pleasant. The Founding Engineering team will work remotely through the summer. Starting September 1, the role will transition to a hybrid model: in-office Monday-Wednesday, with remote flexibility on Thursday and Friday.

Your Impact

In this role, success is measured by the reliability, scalability, and performance of our data platform. You will:

• Lead Technical Design: Own the end-to-end design and delivery of large-scale data ingestion, warehousing, and processing pipelines across billions of daily events-proactively accounting for scalability, failure modes, and security from the start. This includes core data workflows such as the identity graph, ML feature pipelines, and warehouse workload distribution.
• Elevate Reliability: Monitor, troubleshoot, and improve highly available data systems including low-latency data streams and distributed query workloads. Lead blameless post-mortems and implement long-term systemic fixes to prevent incident recurrence.
• Drive Engineering Excellence: Write and optimize complex SQL and Spark queries, extend modern big data tooling (Spark, Flink, Kafka, Iceberg, ClickHouse, AWS EMR/S3), and strengthen the team through high-quality code reviews, technical mentorship, and exemplary technical artifacts such as design docs and architecture diagrams.
• Navigate Ambiguity: Exercise strong judgment to balance long-term data platform health with rapid development velocity-particularly when driving ML feature projection work across large datasets and solving cross-team issues quickly with a small, focused team.
• Collaborate on Direction: Partner with Science, Machine Learning, Product, and Engineering leadership to align technical solutions with business priorities around AI-driven initiatives, including feature engineering, feature projections, and identity graph development.

Tech Stack: Java/Python, Spark, Flink, Kafka, Iceberg, ClickHouse, and AWS services (EMR, S3)

Who You Are

• Experienced Senior Engineer. You bring 7+ years of experience working with a managed language such as Java or .NET, building production-grade systems.
• Deep Spark Practitioner. You have extensive hands-on experience with Spark in production environments, including scaling large datasets in both Spark and SQL.
• Strong Backend Foundation. You have a proven track record designing, decomposing, and delivering high-scale production services or distributed systems.
• Cloud-native engineer. You have experience building and operating systems in cloud environments such as AWS, Azure, or GCP.
• Owner and Driver. You independently drive technical initiatives from problem definition to deployment, taking full accountability for outcomes without needing granular direction.
• Clear Communicator. You can articulate technical tradeoffs and decisions to both technical and non-technical stakeholders across engineering, science, and product teams.

Bonus Points If You Have:

• Experience with high-volume, low-latency data systems
• Background in distributed systems design for large-scale architectures
• Proficiency in Python
• Familiarity with tools such as Flink, ClickHouse, or Kafka

What Success Looks Like in Your First 30/60/90 Days

First 30 Days: Context & Connection

• Build a deep understanding of the data platform architecture, core pipelines, and key areas of technical debt across ingestion, warehousing, and ML feature systems
• Establish relationships with key partners across Data Science, Machine Learning, and Engineering teams
• Contribute to debugging and minor pipeline improvements to learn the production environment and data flows
• Deliverable: Document one existing pipeline workflow or data infrastructure gap to demonstrate system understanding

By 60 Days: Initial Impact

• Own the technical delivery of a meaningful improvement to a core data pipeline, warehouse system, or ML feature workflow
• Identify a gap in data platform documentation or engineering processes and lead the fix
• Participate actively in architectural decision-making and design reviews, particularly around scalability and reliability of high-volume systems
• Deliverable: Propose and implement one improvement to system reliability, query performance, or pipeline efficiency

By 90 Days: Full Ownership

• Independently own a key component of the data platform-such as a critical ingestion pipeline, the identity graph, or ML feature projection workflows
• Deliver a measurable improvement to scalability, latency, or operational performance across billions of daily events
• Be recognized as a trusted technical leader and go-to partner across data science, ML, and engineering teams for your domain

Why This Role

• Scale: Work with some of the largest datasets in the industry and solve real-world problems at a scale few engineers experience
• Impact: Meaningful influence over system design on critical engineering projects from day one
• Collaboration: A highly collaborative, smart, and supportive team environment
• Mission: Build the data infrastructure powering a leading AI-driven advertising platform