Senior Data & Analytics Engineer

About the Role

We're hiring a senior Data & Analytics Engineer on behalf of a US-based private equity firm managing a portfolio of 3 operating companies. This is a long-term, full-time dedicated engagement, you'll be embedded directly into the client's team, working closely with their CTO and Fractional CTO to build their data platform from the ground up.

This isn't a short-term project or a shared resource role. You'll own the entire data layer, ingestion, warehousing, transformation, and BI, and be the person who makes it work reliably at portfolio scale.

What You'll Build

• ETL/ELT pipelines extracting data from field service management platforms (ServiceTitan, BuildOps, Simpro) and QuickBooks Online
• A centralized cloud data warehouse (Snowflake / Azure SQL / Postgres) with Bronze, Silver, and Gold data layers
• A dbt-based transformation layer with dimensional models covering revenue, gross margin, job profitability, EBITDA, and AR/AP aging
• REST API integrations with external platforms including QuickBooks and FSM systems
• Curated BI datasets for Power BI or Sigma dashboards, PE-level consolidated views and PortCo-level restricted reporting
• Pipeline orchestration, monitoring, CI/CD, and data quality frameworks

What We're Looking For

Must-have:

• 4-8+ years in data engineering, analytics engineering, or data warehousing
• Strong Python and advanced SQL
• Hands-on experience building ETL/ELT pipelines and REST API integrations
• Cloud data warehouse experience: Snowflake, Azure SQL/Synapse, or Postgres
• Pipeline orchestration: Airflow, Prefect, or Azure Data Factory
• Dimensional data modeling (star/snowflake schemas)

Nice to have:

• dbt — strong plus, we use it for the transformation layer
• QuickBooks API or financial data integration experience

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• Multi-entity or portfolio-level reporting experience
• Power BI or Sigma experience

The Setup

• 100% remote, full-time dedicated engagement
• Long-term
• You work directly with the client's CTO and Fractional CTO, not through layers of project management
• Competitive compensation, details shared at first call

Who This is For

• You're comfortable working independently, you've owned a data platform before (not just contributed to one), and you don't need someone to define your tasks. You understand that in a PE portfolio context, data accuracy isn't just a technical problem, it's how decisions get made. You care about that.