Data Engineer

Perennial Services Group is a national, private equity-backed platform supporting more than 37 home services brands with centralized infrastructure and expertise. The Data Engineer will build and own the data layer powering performance measurement, including scalable pipelines, Snowflake data models, source-system integrations, data quality, warehouse performance, and analytics-ready datasets for Power BI.

Responsibilities:

• Build and maintain scalable, automated pipelines in Fivetran and dbt that bring data into a unified layer on Snowflake, with the scheduling, testing, and alerting that keep data landing on time
• Design clean, well-documented data models in Snowflake that reflect how the business actually operates, all version-controlled through a Git-based dbt workflow
• Own the integrations across our core operating systems, including Aspire, Real Green, FieldRoutes, ServiceTitan, Acumatica, and ADP, so data flows in cleanly across more than 37 brands
• Own data quality and consistency across systems, monitoring pipelines and catching issues before they ever reach a dashboard
• Keep the warehouse fast and cost-efficient as data volume grows, tuning models and queries and managing Snowflake compute
• Deliver clean, analysis-ready datasets for Power BI and self-service reporting, and turn the analytics team’s needs into reliable models and consistent metric definitions
• Document pipelines, models, and definitions, and help establish the governance and engineering best practices that keep the foundation scalable as PSG grows

Requirements:

• 3 to 6 years in data engineering, analytics engineering, or BI development, ideally in a multi-unit, PE-backed, or high-growth environment
• Strong SQL and real experience building analytical data models, with fluency in a modern stack such as Fivetran, dbt, and Snowflake, or comparable tools
• A track record of building and automating data pipelines end to end, with genuine attention to data quality and reliability
• Comfort connecting multiple source systems into a central warehouse and keeping data flowing cleanly across them
• You work independently, set your own priorities, and build your own structure rather than waiting to be handed one
• A clear, professional communicator who works closely with analysts and stakeholders, explains technical tradeoffs in plain language, and builds trust across the team
• REST APIs and Python (including AWS Lambda)
• orchestration tools such as Airflow or Dagster
• CI/CD for analytics
• DataNest
• Snowflake performance and cost tuning
• exposure to Finance or FP&A workflows
• a major cloud platform such as AWS, Azure, or GCP