Senior Data Engineer

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.