Stord runs on data - every pallet moved, every order fulfilled, and every model our AI team ships depends on data that's fast, clean, and easy to find. As our Senior Data Engineer, you'll own that ecosystem: designing, building, and maintaining the pipelines and infrastructure that turn raw operational data into something the whole business can act on. You'll work closely with data scientists, analysts, and software engineers in a cloud-native environment, building for where Stord is headed, not just where it's been.
We move over $10 billion in commerce a year across fulfillment, warehousing, and supply chain software. You'll build the infrastructure that turns that volume into reliable, governed, ML-ready data - including the foundations our agentic AI tooling runs on.
What You'll Own
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
What You'll Bring
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
Nice to Haves
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
