SENIOR DATA ENGINEER

<p><strong>About Goods &amp; Services</strong><br> <br>Goods &amp; Services is a product design and engineering company. </p> <p>We solve mission-critical challenges for some of the world’s largest enterprises, with deep expertise in highly regulated industries—including <strong>life sciences</strong> and <strong>financial services</strong>. Our <strong>design-led</strong> approach allows us to apply cutting-edge capabilities in <strong>AI,</strong> <strong>Data </strong>and <strong>Hardware Engineering</strong> to companies of any size. </p> <p>Headquartered in the <strong>United States</strong>, we operate regional development centers in <strong>Mexico </strong>and the <strong>United Kingdom</strong>. This global footprint—anchored by our <strong>nearshore </strong>model—enables us to deliver at scale with the speed, efficiency, and cultural alignment our clients expect.</p> <p><strong>About the job</strong><br> <br>Goods &amp; Services is looking for a <strong>Senior Data Engineer</strong> 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.</p> <p><strong>What you’ll do:</strong></p> <ul> <li>End-to-End Pipeline Engineering: Design, build, and deploy scalable ETL/ELT pipelines from diverse source systems into our Snowflake Data Cloud.</li> <li>Cloud Infrastructure: Manage and optimize data flows within an AWS environment (S3, Lambda, IAM), ensuring high availability, security, and cost-efficiency.</li> <li>High-Scale Processing: Leverage Databricks and Python (PySpark) to handle complex data transformations and high-volume workloads.</li> <li>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.</li> <li>Model for Truth: Use dbt to build modular, version-controlled, and tested data models that serve as the definitive foundation for business intelligence.</li> </ul> <p><strong>What you’ll need:</strong></p> <ul> <li>5+ years of experience in data architecture, data engineering, or a closely related discipline in a complex, multi-team data environment</li> <li>Data Warehousing: Expert-level proficiency in Snowflake (clustering, Snowpipe, streams, and tasks) or similar cloud data warehouses.</li> <li>Analytics Engineering: Advanced mastery of dbt and complex SQL transformation logic, with specific experience building semantic models and metric definitions.</li> <li>Big Data &amp; Code: Strong Python skills and hands-on experience with Databricks for Spark-based orchestration.</li> <li>Cloud Infrastructure: Practical experience managing data workloads within AWS.</li> <li>Version Control: Deep understanding of Git-based workflows and CI/CD for data.</li> </ul>