Skechers is looking to define, develop, and scale its enterprise-wide data architecture, implement data governance, enable data observability, and drive consistency in data design and usage across platforms to build a modern, secure, and reliable data foundation for reporting, analytics, and AI/ML use cases.
Requirements
- Proficiency in Python and SQL, with experience building and documenting data pipelines and writing code in notebooks (e.g., Jupyter, Databricks).
- Good understanding of dimensional modeling, data normalization, and SCD (Slowly Changing Dimension) techniques, particularly Type 2.
- Strong understanding of modern data architecture paradigms including medallion architecture, data mesh, and data Lakehouse.
- Some hands-on experience in at least one major cloud platform (AWS, GCP, Azure), ideally AWS (e.g., Glue, Lake Formation, Redshift, Athena).
- Familiarity with data cataloging, lineage tools, and metadata management solutions such as Collibra, Alation, or AWS Glue Data Catalog.
- Good understanding of data governance frameworks, data privacy laws (GDPR, CCPA), and implementation of row/column-level data access controls.
- Knowledge of change data capture (CDC) tools and patterns, Qlik Replicate, or AWS DMS.
Responsibilities
- Work on the design and implementation of enterprise data models, data pipelines, and data platforms to support strategic data initiatives.
- Support and use architectural frameworks that support scalable and secure data processing using Medallion architecture (Bronze/Silver/Gold layers).
- Enforce best practices for SCD Type 2 data modeling, change data capture (CDC), and change tracking across data domains.
- Learn and promote data governance principles including data quality, cataloging, lineage, and stewardship across the enterprise.
- Collaborate with engineering teams to ensure architecture supports both batch and real-time data processing needs.
- Provide architectural guidance for data migration, integration, and modernization efforts across on-premises and cloud-based systems.
- Learn and implement data observability mechanisms for monitoring data reliability, freshness, and schema drift.
Other
- Hybrid work schedule based out of Manhattan Beach, CA office.
- 1+ years of experience in designing, building, and maintaining enterprise data architecture solutions.
- Strong analytical mindset and ability to communicate architectural decisions to both technical and non-technical audiences.
- Familiarity with Agile environments and eagerness to contribute to data transformation projects
- Ability to perform each job responsibility satisfactorily.
