Role overview

As a Senior Data Platform Engineer at Later, you’ll architect and maintain the data infrastructure that powers an AI-driven influencer marketing platform. Later combines proprietary data on billions of social interactions with creator relationships and machine learning to help enterprise brands run confident, results-driven campaigns. This role focuses on building scalable platform systems rather than individual reports or models—you’ll own the foundation that enables analysts, engineers, and data scientists across the company to do their best work.

Key responsibilities

Design and own scalable infrastructure for data ingestion, orchestration, storage, transformation, and consumption, including BigQuery warehouse and lakehouse architecture.
Build reliable batch and real-time data pipelines, develop dbt transformation layers, and evolve orchestration tools and ingestion strategies.
Engineer backend APIs, services, and integrations that securely move data between GCP and AWS environments, managing infrastructure as code and CI/CD deployment automation.
Implement observability, data quality checks, access controls, and cost optimization across the platform while creating reusable tooling and standards.
Make trusted company data accessible for analytics, machine learning, and AI applications by investing in data quality, metadata, lineage, governance, and freshness.
Identify weaknesses in the platform and drive modernization efforts; mentor other engineers and raise technical standards across the team.
Partner with AI/ML, product, and engineering teams to design the platform for emerging AI use cases without requiring major rework.

Requirements

At least six years of experience in data engineering, backend engineering, or platform engineering with a proven track record of owning production data systems at scale.
Strong backend programming skills in Python or a similar language, solid SQL proficiency, and hands-on experience with dbt or comparable transformation frameworks.
Practical cloud experience with GCP and BigQuery, plus comfort working across multiple cloud environments.
Demonstrated experience with infrastructure as code, CI/CD pipelines, and workflow orchestration systems.
Working knowledge of batch and streaming architectures, security, access control, and data governance principles.
Strong judgment in making architectural decisions and communicating tradeoffs to both technical and non-technical stakeholders.

What makes this role worth considering

You’ll work on infrastructure that directly supports Later’s AI-powered products and serves globally recognized brands including Nike, Wayfair, Unilever, and Southwest Airlines. The role offers the opportunity to shape how data flows across a modern multi-cloud environment and to build platforms that other teams depend on and adopt. You can choose to be based in either Boston, MA or Vancouver, British Columbia.