Senior Data Platform Engineer

Later•San Francisco, CA

6d•Remote

About The Position

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. This role is for someone who likes building the system more than the individual report or model. It isn't a traditional ETL/ELT role focused on SQL, dashboards, and pipeline maintenance, and it isn't an ML engineering or data science role. You'll build the data foundation that makes AI work, and other teams will build on top of it.

Requirements

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 Haves

Kubernetes, and experience migrating or redesigning orchestration systems.
Managed ingestion tools like Fivetran.
Experience building internal platforms or tooling that other engineers adopt.
Data foundations for AI, such as vector stores, feature pipelines, or governed LLM access.

Responsibilities

Build, own, and evolve the data platform
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.
Engineer backend systems and keep them reliable
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.
Build the foundation for AI
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.