What You'll Do
1. Apply advanced knowledge of Data Engineering principles, methodologies, and techniques to design and implement data loading and aggregation frameworks across broad areas of the corporation.
2. Gather and process raw, structured, semi-structured, and unstructured data using batch and real-time data processing frameworks.
3. Implement and optimize data solutions in enterprise data warehouses and big data repositories, leveraging distributed processing systems such as Snowflake or Databricks.
4. Design and develop robust data solutions utilizing Kimball data modeling techniques to support scalable analytics and external data products.
5. Develop, test, and maintain data pipelines using Python and dbt (data build tool) for data transformation and modeling tasks.
6. Work closely with product managers and stakeholders to deliver high-quality, external-facing data products, not just internal reporting.
7. Understand and enforce appropriate data master management techniques.
8. Lead the implementation of tools and frameworks for automating the identification of data quality issues.
9. Understand the challenges that the analytics organization faces in their day-to-day work, and partners with them to design viable data solutions.
10. Provide subject matter expertise and guidance for internal and external customers.
11. Play a lead role in planning, providing advice and guidance, mentoring less experienced engineers, and monitoring emerging technologies.
12. Recommend improvements to processes, technology, and interfaces that improve the effectiveness of the team and reduce technical debt.
What We'll Expect From You
13. 5+ years of experience in data engineering, with a focus on data warehousing, ETL/ELT pipelines, and data modeling.
14. Proven experience in designing and implementing data warehouses using the Kimball dimensional modeling methodology.
15. Strong proficiency in Python for data processing and automation.
16. Hands-on experience with dbt for data transformation and testing within the data warehouse environment.
17. Experience with Amazon Web Services (AWS) for data storage, processing, and analytics services.
18. Experience working on data products designed for external customers is highly desired.
19. Experience with Customer Data Platforms (CDP) is a significant bonus.
20. Familiarity with Infrastructure as Code (IaC) principles and tools (, Terraform, CloudFormation) is a bonus.
21. Ability to participate in an on-call rotation to support data platform operations and incident response.