Data Engineer

Integration meets Innovation At Celigo, we believe integration should empower — not exhaust — innovation. As a modern Integration and Automation Platform (iPaaS), we’re on a mission to simplify how companies integrate, automate, and optimize processes. Powered by game-changing technology like runtime AI and prebuilt, mission-critical integrations, Celigo is redefining how businesses connect their world. Celigo is looking for a Data Engineer to contribute to the development, scaling, and maintenance of our Snowflake-based data ecosystem. You will implement and deliver scalable ELT pipelines, production-grade data models, and the observability frameworks that keep them reliable — translating business requirements into well-documented, high-performance data solutions that power analytics, BI, and ML across the organization. Partnering closely with Analytics, Product, and Engineering, you will help enforce data quality standards, align with governance practices, and continuously optimize for cost efficiency and data freshness. You'll bring strong Snowflake experience, a modern dbt-native development mindset, and a fluency with AI-assisted tooling to accelerate your own velocity and collaborate effectively with the team around you. What would you do if hired? • Design, build, and maintain scalable ELT/ETL pipelines and data infrastructure using Snowflake and modern tooling. • Implement data models — including dimensional, data vault, and medallion/lakehouse patterns — to support analytics, BI, and ML use cases. • Collaborate in Architectural discussion and decision making with team • Collaborate with Analytics, Product, and Engineering teams to translate business requirements into reliable, well-documented data solutions. • Establish and enforce data quality standards, automated validations, and observability frameworks to ensure high reliability across all pipelines. • Contribute to data governance practices, including documentation, data lineage, access control, and compliance standards. • Monitor and optimize pipeline performance, compute cost efficiency, and data freshness across the warehouse. • Evaluate and implement emerging Snowflake features and data tooling to improve engineering velocity and reduce costs. • Leverage AI-assisted development tools (e.g., Claude Code, GitHub Copilot, Cursor, or equivalent) to accelerate pipeline development, code review, and documentation. Who are we looking for? Skills & Abilities • Strong hands-on experience with Snowflake, including performance optimization, warehouse configuration, access controls, and cost-efficient development practices. • Strong command of modern ELT tooling such as dbt, Fivetran, Airflow, or Prefect in a Snowflake-native environment. • Hands-on experience integrating AI/ML tools into the development cycle, including AI-assisted coding, automated testing, or LLM-powered data workflows. • Proven ability to translate complex business requirements into scalable, well-documented technical solutions. • Strong communication skills with the ability to collaborate effectively across technical and non-technical teams. Education & Experience • Degree in Computer Science, Engineering, or a related technical field. • 5+ years of hands-on experience in data engineering, specifically building production-grade pipelines and distributed computing frameworks. • 3+ years working directly with Snowflake in a production capacity. • Demonstrated experience in architecting or contributing to at least one full data warehouse migration or rebuild on Snowflake. • Advanced proficiency in SQL and at least one programming language (Python preferred). • Experience with version-controlled, CI/CD-driven data pipeline development (e.g., dbt + GitHub Actions or equivalent). •