Data Engineer

We're building a small, high-ownership data engineering team (2–3 engineers) to power analytics, AI, and business decisions across a growing client portfolio. This is a hands-on, client-facing role with real end-to-end ownership — from raw API ingestion to stakeholder dashboard.

What You'll Own

• Data Pipelines · Airflow + BigQuery → Build & maintain production Airflow DAGs ingesting from ServiceTitan, GoHighLevel, Facebook Ads, Google Ads, and Google Business Profile → Debug silent failures, enforce idempotency, handle CDC lag — zero undetected errors daily → Tenant-level data isolation per client entity in BigQuery
• Transformation Layer · dbt + BigQuery → Own staging & mart models, SCD Type II deduplication (QUALIFY ROW_NUMBER()), incremental merge with partition pruning → Fix transformation bugs, optimize cost & latency, document for analyst self-serve
• Analytics & Reporting · Power BI + Looker Studio → Build trusted dashboards for non-technical stakeholders across 10+ business units → Full Power BI skillset: star schema, DAX, Power Query (M), performance optimization, workspace management
• Data Investigations & Client Communication → Own discrepancy investigations end-to-end — raw tables through dashboard layer → Deliver clear, jargon-free root-cause findings to clients within 24 hours
• Brand Onboarding → Roll new client entities into every system (DAGs, dbt, BigQuery, portals, dashboards) within one sprint

Required Skills

• Python — production DAG authoring, pandas, Flask, REST API integrations
• SQL / BigQuery — window functions, incremental merges, partition pruning, CDC deduplication, cost optimization
• dbt — staging/mart architecture, incremental models, SCD deduplication
• Apache Airflow — step timing, progress logging, Slack + email failure callbacks
• API integrations: ServiceTitan, GoHighLevel, Facebook Ads, Microsoft Graph, Google Sheets, Azure App Service
• Power BI — DAX, Power Query (M), data modeling, performance optimization
• Looker Studio — dashboard maintenance & BigQuery data source management
• Ownership mindset — you notice issues before stakeholders do

Nice to Have

• Google Ads API / Google Business Profile API
• Broader GCP (GCS, Cloud Run, Pub/Sub) or streaming (Kafka)
• Node.js, data observability tooling, CI/CD for dbt & Airflow

Background

Degree in CS, Engineering, or Data Science preferred — but strong hands-on experience delivering production pipelines and client-facing analytics is equally valued. No minimum years required; we care about what you've built and how reliably it runs.

To Apply

Send your resume + a short note covering:

1. A pipeline you built end-to-end (what, why it was hard, how it ran in production)
2. Your strongest BigQuery work (partitioning, clustering, cost optimization, deduplication)
3. Your strongest Power BI or Looker Studio work