Analytics Engineer

Celigo

7d•$120,000 - $140,000•Remote

About The Position

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 an Analytics Engineer to serve as the definitive owner of our business logic and the primary architect of our "Gold" data layer. You will bridge the gap between raw data and executive-level insights by transforming complex datasets into clean, validated, and highly performant tables. By applying software engineering rigors—such as version control, automated testing, and CI/CD—to our workflows, you will ensure our data warehouse remains a trusted "single source of truth." Your mission is to empower the company with the reliable data to drive decisions across Product, Finance, and GTM teams.

Requirements

Strong belief in applying software engineering best practices (testing, versioning, peer review) to data workflows.
Ability to design modular data models (Star Schema, Kimball) that balance flexibility with system performance.
Proficiency in communicating technical trade-offs to both analysts and infrastructure engineers.
Degree in Computer Science, Mathematics, Statistics, or a related quantitative field.
3+ years of experience in Analytics Engineering, Data Engineering, or a technical Data Analyst role.
Expert-level proficiency in SQL with a focus on writing efficient, maintainable, and readable code.
Hands-on experience with dbt / Coalesce and modern cloud data warehouses like Snowflake or BigQuery.
Familiarity with Git, pull request workflows, and command-line interfaces.

Nice To Haves

Experience with Python for data orchestration (e.g., Airflow) or advanced data manipulation.
Understanding of Fintech or SaaS-specific metrics such as ARR, churn, and financial reconciliation.

Responsibilities

Develop and maintain scalable data models using dbt / Coalesce to transform raw data into clean, documented, and production-ready schemas.
Collaborate with Data Engineering and Data Analytics to ensure technical architecture supports business logic and reporting requirements.
Implement automated testing, data documentation, and observability frameworks to ensure a "single source of truth."
Optimize SQL queries and warehouse configurations to improve performance and manage compute costs.
Maintain version control workflows, CI/CD pipelines, and coding standards across the data team.
Support the semantic layer and BI data catalogs to enable self-service exploration.