Senior Data Platform Engineer - Later, Vancouver

About the role

Later is the world's most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. As a Senior Data Platform Engineer at Later, you'll own the foundation our data ecosystem runs on. You'll design, build, and evolve the infrastructure, backend services, and platform capabilities that move data from source to decision, and that help engineers, analysts, and data scientists do their best work.

This is a data engineering role with a platform mindset. Our analytics platform runs on GCP and BigQuery, and our operational databases live in AWS. You'll shape how data moves between them, modernize what we have, and get our data ready for the AI-powered products we're building. You'll treat the platform as a product and the teams who depend on it as your customers.

What you'll do

You will be responsible for building, owning, and evolving the data platform, engineering backend systems, and establishing the foundation for AI.

• Design and own scalable infrastructure across ingestion, orchestration, storage, transformation, and consumption, including our BigQuery warehouse and lakehouse.
• Make architectural decisions, explain the tradeoffs, and modernize the parts that have outgrown their original design.
• Build reliable batch and real-time pipelines and dbt transformation layers.
• Evolve our orchestration (GCP-native tools plus our custom orchestrator) and shape our ingestion strategy, including how we evaluate Fivetran against custom connectors.
• Spot weaknesses in the platform and drive the fixes, mentor other engineers, and raise the technical bar.
• Build the APIs, services, and integrations that move data in and out of the platform, including secure, cost-aware data movement between GCP and AWS.
• Manage infrastructure as code and automate deployment with CI/CD.
• Build observability, data quality checks, and access controls, and optimize performance and cost.
• Create reusable tooling and standards that make the right way the easy way.
• Make trusted company data accessible for analytics, machine learning, and AI applications, including LLMs and AI agents.
• Invest in data quality, metadata, lineage, governance, and freshness.
• Design the platform to absorb new AI use cases without major rework, and partner with AI/ML, product, and engineering teams on what they need.

Skills and experience

We are looking for candidates who bring solid technical background and experience in data and platform engineering.

• 6+ years in data engineering, backend engineering, or platform engineering, with a track record of owning production data systems at scale and improving their reliability, performance, and cost.
• Strong backend programming skills (Python or similar), solid SQL, and experience with dbt or a similar transformation framework.
• Hands-on cloud experience, ideally GCP and BigQuery, and comfort working across more than one cloud.
• Experience with infrastructure as code, CI/CD, and workflow orchestration.
• Experience with batch and streaming architectures, and a working understanding of security, access control, and data governance.
• The judgment to make architectural decisions and explain the tradeoffs to technical and non-technical partners.
• Nice to have: Kubernetes, and experience migrating or redesigning orchestration systems.
• Nice to have: Managed ingestion tools like Fivetran.
• Nice to have: Experience building internal platforms or tooling that other engineers adopt.
• Nice to have: Data foundations for AI, such as vector stores, feature pipelines, or governed LLM access.

Perks and benefits

We take a market-based and data-driven approach to compensation. Salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.