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About Goods & Services
Goods & Services is a product design and engineering company.
We solve mission-critical challenges for some of the world’s largest enterprises, with deep expertise in highly regulated industries—including life sciences and financial services. Our design-led approach allows us to apply cutting-edge capabilities in AI, Data and Hardware Engineering to companies of any size.
Headquartered in the United States, we operate regional development centers in Mexico and the United Kingdom. This global footprint—anchored by our nearshore model—enables us to deliver at scale with the speed, efficiency, and cultural alignment our clients expect.
About the job
Goods & Services is looking for a Senior Data Engineer to lead the development and scaling of our core data infrastructure. You won’t just move data; you will be a key contributor in architecting and maintaining our Sources of Truth. Your mission is to transform raw, source data into authoritative, governed data marts by building high-performance pipelines and a robust Semantic Layer that ensures consistency across the entire business.
What you’ll do:
- End-to-End Pipeline Engineering: Design, build, and deploy scalable ETL/ELT pipelines from diverse source systems into our Snowflake Data Cloud.
- Cloud Infrastructure: Manage and optimize data flows within an AWS environment (S3, Lambda, IAM), ensuring high availability, security, and cost-efficiency.
- High-Scale Processing: Leverage Databricks and Python (PySpark) to handle complex data transformations and high-volume workloads.
- Implement the Semantic Layer: Collaborate with the team to define, implement, and scale our Semantic Layer (via dbt Semantic Layer, MetricFlow, or similar) to standardize business logic, metrics, and dimensions for all downstream consumers.
- Model for Truth: Use dbt to build modular, version-controlled, and tested data models that serve as the definitive foundation for business intelligence.
What you’ll need:
- 5+ years of experience in data architecture, data engineering, or a closely related discipline in a complex, multi-team data environment
- Data Warehousing: Expert-level proficiency in Snowflake (clustering, Snowpipe, streams, and tasks) or similar cloud data warehouses.
- Analytics Engineering: Advanced mastery of dbt and complex SQL transformation logic, with specific experience building semantic models and metric definitions.
- Big Data & Code: Strong Python skills and hands-on experience with Databricks for Spark-based orchestration.
- Cloud Infrastructure: Practical experience managing data workloads within AWS.
- Version Control: Deep understanding of Git-based workflows and CI/CD for data.
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