Data Engineer

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Ledgy

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Finance • Fintech • SaaS

Ledgy is a modern equity management platform designed to streamline equity workflows for private and public companies. It provides comprehensive solutions such as cap table management, equity plan automation, employee engagement, financial reporting, and compliance. Ledgy's platform gives users the ability to automate manual processes, manage data efficiently, and stay audit-ready across jurisdictions. It is highly focused on providing real-time insights and access, ensuring that employee and stakeholder communications are clear and effective. Ledgy is committed to offering robust security and compliance measures, making it a reliable choice for international teams looking to manage equity seamlessly and accurately.

51 - 200 employees

💸 Finance

💳 Fintech

☁️ SaaS

📋 Description

• Manage and optimize data infrastructure and ETL pipelines using Fivetran, Airbyte, and Google Cloud Platform, ensuring reliable data flow from multiple sources into our analytics ecosystem • Develop, test, and maintain DBT models that transform raw data into analytics-ready datasets following best practices • Create and manage LookMLmodels in Looker to enable self-service analytics for stakeholders across the company • Drive continuous improvement of our data engineering practices, tooling, and infrastructure as a key member of the Operations team

🎯 Requirements

• 2-3+ years experience building production data pipelines and analytics infrastructure, with DBT, SQL, and Python (Pandas, etc.) • Experience implementing and managing ETL/ELT tools such as Fivetran or Airbyte • Ideally hands-on experience with GCP (BigQuery) • Proficiency in Looker, including LookML development • Strong plus if you have experience using n8n or similar automation tools • Experience with SaaS data sources (HubSpot, Stripe, Vitally, Intercom) • Familiarity with AI-powered development tools (Cursor, DBT Copilot) and a strong interest in leveraging cutting-edge tools to improve workflow • Strong problem-solving skills and ability to debug complex data issues • Excellent communication skills with ability to explain technical concepts to non-technical stakeholders.