Data Platform Engineer – Contract

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Lola Blankets

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2021

🛍️ eCommerce

👥 B2C

eCommerce • B2C

Lola Blankets is a consumer-focused e-commerce brand that designs, markets, and sells plush faux-fur and weighted blankets, pillows, pet accessories, candles, and related home goods through an online Shopify storefront. The company emphasizes soft, machine-washable, OEKO-TEX® certified materials, contemporary design, gifting and corporate programs, and operates a charitable initiative (Blankets for the Brave) supporting breast cancer patients.

📋 Description

• 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

🏖️ Benefits

• Fixed monthly fee, set based on experience and capabilities • Option to convert to full-time role based on performance and organizational structure