Senior Data Engineer

Aeolus Data Solutions is a boutique, founder-led data engineering practice for startups andscale-ups across North America. We design, build, and audit the production data pipelines andAI-ready data foundations that analytics and AI initiatives actually run on. Our thesis is simple:

AI projects don’t fail on the model — they fail on the data pipeline underneath it.

We bring Big Tech engineering discipline (CI/CD, automated testing, infrastructure-as-code, and versioned pipelines) to companies too lean to hire it in-house, and a senior practitioner works directly with every client. No account managers. No junior delivery bench. No handoffs.

You’ll be the senior engineer on client engagements from first audit to production handoff.

That means going deep on an unfamiliar stack in week one, finding the schema drift and silent pipeline failures nobody caught, and then building the foundation that holds. One month you’re hardening a RAG ingestion pipeline; the next you’re rebuilding a scale-up’s warehouse or embedding as their fractional data lead. You own the work and the client relationship directly —this is a senior seat, not a ticket queue.

• Run data & AI-readiness audits. Assess a client’s pipelines, schemas, metadata, and warehouse; write the technical report and the prioritized remediation roadmap that follows.
• Build production data platforms. ELT/ETL pipelines, dbt models and tests, orchestration(Airflow/Dagster), warehouse and lakehouse builds (Snowflake/Databricks), and Terraform-managed infrastructure with CI/CD for data.
• Make data AI-ready. Semantic layers, metadata cataloging, clean lineage, and RAG-ready ingestion with validation before context ever reaches a model endpoint.
• Provide fractional data leadership. Embed with a client’s team a few days a week — set engineering standards, standardize KPIs, guide platform decisions, and mentor their engineers.
• Own the relationship. Scope work, explain tradeoffs to a CTO, and deliver — directly, withno manager relaying messages in between.

• 5+ years building and operating production data pipelines. You’ve owned data infrastructure in production, not just prototypes.
• Depth in the modern data stack: dbt, at least one cloud warehouse/lakehouse(Snowflake, Databricks, or Big Query), an orchestrator (Airflow, Dagster, or Prefect), a major cloud (AWS/GCP/Azure), and strong Python + SQL.
• Software-engineering discipline applied to data: version control, automated testing(dbt tests, Great Expectations), CI/CD, and infrastructure-as-code (Terraform).
• You ramp fast on messy, unfamiliar systems. Consulting means a new stack and real-world data quality problems every engagement — you find your footing quickly.
• You’re client-facing. You can explain a technical tradeoff to a non-engineer, write a clear audit report, and be the calm senior voice in the room.
• You’re self-directed. Small team, high ownership, no one assigning you tickets.

• Warehouse cost optimization / Fin Ops (query tuning, clustering, spend containment).
• Experience at Big Tech scale or high-growth data teams.
• Prior consulting, agency, or fractional/embedded experience.

• Variety by design — many stacks, industries, and problems; greenfield builds and rescue jobs.
• Senior-only shop — your name is on the work, and the quality bar is the whole product.

• Employment type: Full-time, or contract if that suits you better.
• Location / work authorization: remote within Canada or the United States; you must be authorized to work where you’re based. We’re incorporated in both British Columbia and California.
• Compensation:
  • United States: US $135,000–US $175,000 / year
  • Canada: CA $95,000–CA $125,000 / year
• Start: Flexible — we move at the pace of finding the right person.