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PermitFlow

Analytics Engineer

New York City, NY (Remote)$125k–$215kfulltimemidAdded 1 month ago

About this role

PermitFlow is seeking an Analytics Engineer to enhance its AI-driven data infrastructure for the construction industry. The role involves data modeling, pipeline management, and collaboration with various teams to support business intelligence and decision-making processes.

What you'll do

  • Design and implement scalable data models for analytics and reporting.
  • Build and maintain efficient data pipelines for transforming datasets.
  • Collaborate with teams to integrate data from various sources.
  • Implement data governance practices to ensure quality and security.
  • Deliver analytics solutions and develop dashboards for stakeholders.
  • Manage and optimize the data stack for reporting and insights.

What they're looking for

  • 3+ years in analytics engineering or related roles.
  • Proficiency in PostgreSQL, SQL, dbt, or similar.
  • Experience with ETL tools and data stacks.
  • Programming skills in Python or similar languages.
  • Designing data models for analytics purposes.
  • Understanding of data governance and quality practices.
  • Strong communication skills for explaining technical concepts.
  • Familiarity with business intelligence tools and cloud platforms.

Benefits

  • Competitive salary and equity options.
  • 100% company-paid medical, dental, and vision coverage.
  • 401(k) savings plan.
  • Unlimited PTO and paid family leave.
  • Home office equipment stipend.
  • Daily meals provided in-office.
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PermitFlow

PermitFlow builds AI-driven software solutions that streamline permitting and pre-construction workflows in the construction industry. The company is hiring engineers across design systems, full-stack development, applied AI, customer deployment, and analytics to enhance its platform's capabilities and user experiences.

Website
permitflow.com
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Likely interview questions

  • Walk us through a data pipeline you've built from scratch—what sources did you integrate, and how did you ensure data quality and reliability?
  • Describe your experience with dbt. How have you used it to build and maintain data models, and how do you approach testing and documentation?
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