Data Platform Engineer – Contract

Lola Blankets

Data Platform Engineer at Lola Blankets responsible for data ingestion and analytics platform management. Collaborating with teams to improve data quality and infrastructure across the organization.

Posted 6/15/2026contractRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowCloudETLPythonSQLTypeScript

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data engineeringdbtSnowflakePythonTypescriptSQLDevOpsdata quality standardsreverse ETLA/B testing

Soft Skills

problem-solvingcross-team collaborationindependencecommunicationchange managementdebuggingadaptabilityownershipproactive issue resolutiondocumentation