Skechers is looking to define, develop, and scale its enterprise-wide data architecture to support reporting, analytics, and AI/ML use cases. This involves implementing data governance, enabling data observability, and driving consistency in data design and usage across platforms to build a modern, secure, and reliable data foundation.
Requirements
- Advanced proficiency in Python and SQL, with extensive experience in designing, building, and documenting complex data pipelines and developing scalable code within notebook environments (e.g., Jupyter, Databricks).
- Expert-level knowledge of dimensional modeling, data normalization, and advanced Slowly Changing Dimension (SCD) methodologies, with a focus on Type 2 implementations.
- Deep familiarity and hands-on leadership with cutting-edge data architecture frameworks, including medallion architecture, data mesh, and Lakehouse paradigms.
- Extensive hands-on experience with leading cloud platforms (AWS preferred), including services such as Glue, Lake Formation, Redshift, and Athena, driving cloud-native data solutions.
- Proven track record with enterprise-grade data cataloging, lineage, and metadata management tools such as Collibra, Alation, or AWS Glue Data Catalog to ensure data governance and quality.
- In-depth knowledge and practical implementation experience with data governance frameworks, regulatory compliance (GDPR, CCPA), and granular data access controls at row and column levels.
- Expertise in change data capture (CDC) technologies and architectures, including tools like Qlik Replicate, or AWS DMS for real-time data synchronization.
Responsibilities
- Lead the design and implementation of enterprise data models, data pipelines, and data platforms to support strategic data initiatives.
- Develop architectural frameworks that support scalable and secure data processing using Medallion architecture (Bronze/Silver/Gold layers).
- Define and enforce best practices for SCD Type 2 data modeling, change data capture (CDC), and change tracking across data domains.
- Establish 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.
- Design and implement data observability mechanisms for monitoring data reliability, freshness, and schema drift.
Other
- 8+ years of proven expertise in architecting, developing, and optimizing large-scale enterprise data architecture solutions.
- Hybrid work schedule based out of Manhattan Beach, CA office.
- Exceptional analytical skills combined with the ability to articulate complex architectural strategies and decisions clearly to both technical teams and business stakeholders.
- Demonstrated success in leading data architecture initiatives and aligning technical design with business needs.
- Experience working in Agile environments and participating in enterprise-level data transformation programs.
