Google Cloud Data Engineer

NMI is a company that enables partners with innovative payment solutions. They are seeking a skilled Mid-Level Data Engineer to build, maintain, and improve data pipelines and models for analytics and business intelligence, while collaborating with various teams to ensure data accuracy and reliability.

Responsibilities:

• Build and maintain production-grade ELT pipelines that ingest data from internal applications, third-party SaaS tools, and event streams into our BigQuery data warehouse
• Own specific data domains end-to-end — from raw ingestion through to marts — ensuring your areas of the warehouse are accurate, tested, and well-documented
• Write and maintain dbt models, tests, macros, and documentation within our established dbt project conventions and code review process
• Develop and manage Airflow DAGs on Cloud Composer or other similar tools to orchestrate data workflows, following patterns and standards set by the team
• Implement data quality checks and monitoring to catch anomalies before they reach downstream consumers
• Optimize BigQuery queries and models for cost and performance within your domain, escalating architectural tradeoffs to senior engineers when appropriate
• Collaborate with analysts and stakeholders to translate business data needs into well-scoped pipeline and modeling tasks
• Participate in on-call rotations, respond to pipeline incidents, and write clear postmortems
• Contribute to team documentation and runbooks so that your work is maintainable by others

Requirements:

• 3–5 years of experience in data engineering or a closely related data infrastructure role
• Proven experience designing and implementing scalable data pipelines and warehouse architectures
• Strong expertise in Google Cloud Platform (BigQuery, Cloud Storage, Cloud Composer, Pub/Sub, Dataflow)
• Hands-on experience with dbt (data build tool) — models, tests, macros, sources, and documentation — at production scale
• Experience building and maintaining data pipelines with Apache Airflow or a comparable workflow orchestration tool
• Strong proficiency in SQL, including advanced BigQuery SQL (window functions, partitioning, clustering, query optimization)
• Proficiency in Python for data engineering tasks, including API integrations, data processing scripts, and custom operators
• Familiarity with data modeling concepts: star schema, dimensional modeling, slowly changing dimensions (SCD)
• Experience with version control (Git) and collaborative development workflows (pull requests, code review)
• Understanding of data quality, lineage, and observability best practices
• Startup or growth-stage mindset — comfortable with ambiguity, rapid iteration, and evolving priorities
• Excellent communication skills, with the ability to collaborate effectively across technical and non-technical teams
• Experience with Terraform or similar infrastructure-as-code tools for managing cloud data infrastructure
• Familiarity with streaming technologies such as GCP Pub/Sub, Dataflow, or Apache Kafka
• Knowledge of Looker, Tableau, or other BI tools and how data models power them
• Google Cloud Professional Data Engineer certification