Data Engineer

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 and maintaining ELT pipelines, and ensuring data quality for various teams within the company.

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
• Hard requirement: 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