Data Platform Engineer

Job Overview

Lola Blankets is a fast-growing direct-to-consumer brand behind some of the internet’s most-loved blankets. Our open-source-first data stack (dbt, Dagster, Lightdash, and migrating from Snowflake to MotherDuck) powers key decisions, so we keep it reliable, fast, and actionable. 

We’re hiring a Data Platform Engineer to sit at the intersection of data and engineering—owning the analytics platform foundation while supporting the broader engineering roadmap across product, operations, and integrations. 

On the data side, you’ll partner with our Analytics Lead to own ingestion, transformation, orchestration, and the semantic layer. When a dashboard number looks off, you’ll trace it through Lightdash/dbt/pipelines, find the root cause, and fix it. 

On the engineering side, you’ll work with our Technology & Engineering Lead on integrations, event pipelines, and platform infrastructure, applying a DevOps mindset to environments, deployments, and production reliability. 

We’re a lean, builder team: open-source-leaning, fast-moving, and opinionated. You’ll be expected to bring strong judgment and the execution to match.

Core Responsibilities

Data Platform & Pipeline Ownership 

• 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 

Data Observability & Quality 

• 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 

Engineering Support & Integrations 

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