dbt Cloud Data Engineer

About the Role

This role is responsible for the daily operation of dbt jobs: building models, processing complex and high-volume datasets efficiently, monitoring execution, resolving failures, and ensuring reliable outputs for data products, analytics, and AI use cases. Working closely with the dbt Cloud Solution Architect, Data Engineer, and Data Scientists on our team, you will design and refine SQL models to deliver on business requirements. You will fine-tune strategies for incremental processing and materialization, move changes through bronze/silver/gold layers into downstream data products, and enhance data CI/CD processes.

Key Responsibilities

• Develop, test, document, deploy, and operate dbt models and jobs each day in dbt Cloud and Snowflake, transforming Global Customer Success source data into reliable, reusable data products.
• Process complex, high-volume datasets efficiently by applying appropriate model boundaries, incremental logic, materializations, scheduling, warehouse use, and reuse of transformed data.
• Monitor dbt job execution, runtime, failures, regressions, and Snowflake compute consumption; investigate issues and implement optimizations that reduce processing workload and cost.
• Work with and help develop the CI/CD, code-review, testing, release, and documentation standards for our growing data team.
• Work with data scientists, dashboard developers, and AI application owners to verify that production outputs meet downstream requirements, while maintaining models, tests, lineage, runbooks, and technical documentation that build long-term internal capacity.
• Define use of cutting-edge dbt features such as semantic view deployment, dbt Fusion, and dbt Wizard.

Requirements & Tech Stack

• Advanced, hands-on expertise with dbt Cloud, including daily development and operation of production transformations—this is a non-negotiable requirement for the role.
• Advanced SQL skills and experience using Snowflake or a comparable cloud data platform to transform complex, high-volume datasets efficiently.
• Strong working knowledge of dbt models, sources, tests, macros, packages, documentation, lineage, incremental processing, snapshots, materializations, and job orchestration.
• Experience monitoring job execution and diagnosing failures, regressions, runtime, warehouse behavior, and compute consumption, with evidence of implementing workload and cost optimizations.
• Experience working across separated development, quality, and production environments with Git-based CI/CD practices, including branching, pull requests, automated testing, controlled promotion, release validation, and rollback support.
• Ability to operate and support production dbt workloads, document resolutions and operating procedures, and communicate risks, tradeoffs, and downstream impacts to technical and business partners.

Qualifications

• Proficiency with Python or another scripting language for data validation, automation, or pipeline support.
• dbt certification or a portfolio demonstrating substantial dbt delivery experience.
• Experience with Snowflake performance, workload monitoring, and cost optimization.
• Experience building data products for telemetry, customer health, SaaS, sales, services, or Customer Success use cases.
• Familiarity with APIs, file-based ingestion, orchestration tools, data quality frameworks, and metadata/catalog platforms.
• Experience working in a globally distributed Agile or product-oriented data team.