Senior Data Platform Engineer
Overview:
As a Senior Data Platform Engineer at Later, you own the data foundation that powers our analytics and AI-enabled products. You will design, build, and evolve the data platform spanning ingestion, storage, transformation, and consumption across GCP/BigQuery and AWS. You'll treat the platform as a product, collaborating with engineers, analysts, and data scientists to enable scalable, reliable data workflows and AI readiness. This role combines architecture, platform stewardship, and hands-on development to shape data-driven decision making at scale. Compensation / Benefitsremote-friendly opportunitiesbenefits planscompetitive compensationinclusive workplacediversity & inclusion initiativesequal opportunity employer
Responsibilities:
- Design and own scalable data infrastructure (ingestion, orchestration, storage, transformation, consumption) including BigQuery warehouse/lakehouse
- Build reliable batch and real-time pipelines and dbt layers
- Evolve orchestration and ingestion strategy, evaluating connectors like Fivetran
- Identify and fix platform weaknesses; mentor engineers to raise the technical bar
- Develop APIs, services, and data movement between GCP and AWS with IaC and CI/CDImplement observability, data quality checks, access controls, and cost/performance optimizations
- Create reusable tooling and standards; enable AI readiness by supporting analytics, ML, and AI applications
- Partner with AI/ML, product, and engineering teams to accommodate new AI use cases and governance
Key requirements:
- 6+ years in data/backend/platform engineering with production data systems at scale
- Strong backend programming (Python or similar) and SQL, with dbt or equivalent transformation framework
- Hands-on cloud experience (GCP and BigQuery), comfort with multiple clouds
- Experience with infrastructure as code, CI/CD, and workflow orchestration
- Experience with batch and streaming architectures, security, access control, and data governance
- Ability to make and communicate architectural tradeoffs to technical and non-technical stakeholders
- Mentorship and ability to raise the technical bar
- Strong communication to explain tradeoffs to diverse partners
- Collaborative, results-driven mindset
- PythonSQLdbt or similar transformation framework