Data Platform Engineer

Lola Blankets is a fast-growing comfort and lifestyle brand on a mission to make the world a cozier place. They are seeking a Data Platform Engineer to own the analytics platform foundation and support the broader engineering roadmap across product, operations, and integrations. The role involves managing data pipelines, ensuring data quality, and supporting engineering initiatives with a focus on DevOps practices.

Responsibilities:

• Own our data ingestion layer end-to-end, including completing our migration to open-source ingestion tooling (dlt) and maintaining reliability as the stack evolves
• Manage dbt models, tests, documentation, and the semantic layer - the definitions that determine what every metric means across the business
• Own Dagster orchestration: scheduling, retries, alerting, and failure handling across all pipeline runs
• Keep Lightdash metadata, dimension/measure definitions, and access controls accurate and current
• Accelerate data refresh cycles to support near-real-time operational use across the business
• Build monitoring, failure alerting, and anomaly detection into the stack so issues surface proactively
• Chase data through systems when things go wrong: trace why records drop or transform unexpectedly between source and dashboard, and resolve the root cause rather than the symptom
• Establish and document data quality standards and lineage practices across the warehouse
• Partner with the Director of Strategy & Analytics — and the Technology Lead once that role is filled — on platform infrastructure, system integrations, and technical initiatives where data is a core component
• Build and maintain reverse ETL pipelines to push warehouse data back into operational tools
• Contribute to A/B testing infrastructure and the systems that support consistent metric definitions across the org
• Own separation of dev and production environments: deployment pipelines, change management, access controls, and release practices
• Maintain infrastructure documentation and ensure the platform is operable beyond any single person

Requirements:

• 3+ years of data engineering or data platform experience - you've owned production pipelines, not just built them in a sandbox
• Strong dbt skills: models, tests, sources, exposures, and the semantic layer
• Solid Snowflake or equivalent cloud warehouse experience (MotherDuck is where we are likely to land shortly)
• Hands-on with a modern orchestration tool (Dagster, Airflow, Prefect, or similar)
• Strong Python or Typescript plus SQL - enough to read, debug, and write anything in the stack
• DevOps experience: you think in terms of environments, deployments, change control, and what happens when things break in production
• Open-source bias - you'd rather build and own something than pay for a managed tool that abstracts away control
• Comfortable with GenAI-assisted development: using LLMs as part of your development workflow to move faster and write better code
• Comfortable debugging data end-to-end - you can trace a wrong number back through the semantic layer, dbt models, and ingestion pipeline to the source
• Works across team boundaries comfortably; this role sits between data and engineering and requires interfacing with leaders from both teams
• Works well independently in a lean team with minimal process overhead
• Experience in DTC, eCommerce, or a fast-moving consumer business a plus