Data Engineer (Contract)

Contrarian Thinking is building the infrastructure layer for modern entrepreneurs, and they are seeking a Data Engineer to transform messy data into reliable, trustworthy information. The role involves owning the data layer, building ELT pipelines, and ensuring data quality across various teams and products.

Responsibilities:

• Build and own ELT pipelines that sync data from HubSpot and other sources into GCP (Fivetran and comparable tools), reliably and on schedule
• Model raw data into clean, trusted, analysis-ready tables in dbt (staging, intermediate, and mart layers)
• Design and maintain the warehouse across PostgreSQL and BigQuery, tuned for the questions our products and teams actually ask
• Handle messy, ambiguous, real-world data: reconcile inconsistencies across sources and build pipelines that fail loudly, not silently
• Run reverse ETL to push modeled data back into HubSpot and other business tools, so teams work off the latest state
• Own data quality and observability: dbt tests, freshness checks, monitoring, and alerting that catch problems before anyone downstream notices
• Define and maintain the core metrics and the logic underneath them, so definitions stay consistent and 'my numbers don't match yours' goes away
• Keep the data that powers our products reliable and on time, including safe schema changes and migrations
• Automate the repetitive parts of the pipeline so they run themselves

Requirements:

• 5+ years building and maintaining data pipelines and warehouses in production
• Strong with dbt: transformation layers, tests, materializations, and well-structured projects (staging, intermediate, and mart layers)
• Hands-on with ELT tools like Fivetran (or Airbyte, Stitch, custom Python) to sync sources like HubSpot into a warehouse
• Deep BigQuery experience (or comparable warehouses), including performance tuning, partitioning, and cost awareness
• Comfortable with PostgreSQL for both transactional and analytical workloads
• Strong SQL as your primary language, plus Python for scripting and automation
• A track record with messy, ambiguous, real-world data: investigating quality issues, reconciling sources, and handling edge cases without silent failures
• Comfortable with Git and CI/CD for data pipelines
• You care about outcomes: data people trust, pipelines that don't break, numbers that match
• You must be available during US business hours, 9am to 5pm Central Time (CST/CDT), on weekdays
• Built an ingestion and transformation pipeline from scratch, from sources to trusted marts
• Synced HubSpot or another CRM into a warehouse and kept it reliable
• Set up reverse ETL (Census, Hightouch, or similar) to push data back into business tools
• Experience with data orchestration (Airflow, Dagster, Prefect)
• Built data quality and freshness monitoring that caught problems before stakeholders did
• High-growth startup, solo builder, or high-ownership environment