Senior Data Engineer (GCP)

Design and build scalable data pipelines using GCP services like Cloud Dataflow, Cloud Pub/Sub, Data Stream, Spark, and Cloud Composer, ingesting, processing, and storing large datasets from multiple sources

Develop, test, and maintain data models, schemas, and ETL (Extract, Transform, Load) processes using BigQuery, Cloud SQL, Spark, and Data Studio

Partner with stakeholders to understand business requirements and translate them into data infrastructure that actually solves the problem

Optimize data pipelines for performance, scalability, and cost efficiency, using GCP-native tools such as Dataproc and Bigtable

Ensure data quality, integrity, and security by building validation processes and enforcing data governance, access control, and security best practices

Automate workflows using Cloud Composer so pipelines run reliably and on schedule, not by accident

Own pipeline and data infrastructure reliability end-to-end — monitoring, alerting, troubleshooting, and root cause analysis — partnering with SRE and Infrastructure on shared GCP resources (compute, storage, network) rather than handing reliability off to someone else

Prepare, model, and expose data for ML and agentic AI use cases, including the governed data pipelines Stord's AI tooling queries in production

Mentor the broader data team as a senior technical voice on pipeline design and data infrastructure standards

Stay ahead of GCP's roadmap and emerging best practices, and put what you learn back into the platform

5+ years of data engineering experience, including at least three years in a cloud environment (preferably GCP)

Expertise across GCP services: BigQuery, Dataflow, Pub/Sub, Cloud Composer, Cloud Storage, Cloud Functions, and Cloud SQL

Strong SQL, with real experience writing complex queries for data extraction and analysis

Hands-on ETL development and workflow orchestration using tools like Apache Airflow

Proficiency in Python and/or Spark for data processing and pipeline development

Experience with streaming data pipelines (Pub/Sub, Dataflow, or similar) as well as batch processing

A solid grasp of data warehousing concepts, large datasets, and query optimization

Working knowledge of data governance, security, and compliance in a cloud environment

Understanding of ML concepts and data preparation for ML applications, with interest in agentic AI tooling a must

Strong analytical, problem-solving, and troubleshooting instincts, with real attention to detail

Clear, confident communication and the ability to collaborate across teams

Bachelor's degree in computer science or a comparable field, or equivalent experience

GCP certification (Professional Data Engineer or Cloud Architect)

Experience with ML infrastructure on GCP, such as AI Platform and TensorFlow

Familiarity with DevOps/MLOps practices — CI/CD, infrastructure as code (Terraform), and containerization (Kubernetes, Docker) on GCP

Experience with other data processing frameworks, such as Apache Spark

A background in logistics, supply chain, 3PL, or warehouse/fulfillment technology

Experience working on a distributed team