Data Engineering Support for Retail Apparel Company

Organization

Our client is a renowned retail apparel chain headquartered in Texas, operating over 1,500 stores across the US and Canada. As a leader in the fashion retail industry, the company has strategically acquired several prominent market players as subsidiaries, further expanding its market presence. Backed by a robust supply chain and warehouse network, their team of approximately 22,500 employees provides a high-touch, personalized shopping experience, driving loyalty across all their brands.

Challenge

Our client operates an Enterprise Data Warehouse, initially built on Teradata and later migrated to Snowflake during 2019–2020. The ecosystem also includes departmental reporting data marts on Hyperion Essbase and MicroStrategy. The broader technology landscape integrates tools such as DataStage, Python, UC4, MySQL, and Oracle. A tightly monitored data pipeline is critical, as high-visibility reports must be available to end users daily.

Ongoing enhancements and maintenance tasks include adding new aggregate tables, developing new ETL jobs, addressing production issues through code or data fixes, and provisioning data for Data Scientists. The daily ETL processes run from 12:00 AM to 5:30 AM, ensuring timely data availability for business operations.

The client required high-efficiency support with defined L1, L2, and L3 ownership to ensure seamless operations. This effort demanded robust coordination among various teams, including Production Operations, ETL/DW Support, DBAs, and other infrastructure units.

Solution

The critical timing and nature of the daily data pipeline demanded specialized expertise in Data Engineering and Data Warehouse support. XTIVIA was a natural choice for this role, given our deep involvement in implementing the client’s ETL pipelines and our comprehensive knowledge of their data, systems, data warehouse platforms, and technology landscape. XTIVIA performed the following support activities:

• Delivering 8×7 data pipeline support to maintain operational continuity.
• Monitoring ETL jobs and promptly responding to and resolving production issues.
• Collaborating with vendors on development, enhancement, and support activities.
• Identifying and addressing data and coding issues to maintain data integrity.
• Implementing ongoing enhancements and fixes to the data warehouse infrastructure.
• Performance tuning existing ETLs to improve efficiency and reduce processing time.
• Transforming complex and slow-performing reporting logical data marts into aggregate tables within the Enterprise Data Warehouse (EDW).
• Providing database administration support for MySQL and Oracle platforms.
• Preparing data required for Data Science pipelines.
• Developing new ETL jobs leveraging Snowflake, SQL, and UC4.

BUSINESS RESULT

• Enhanced operational efficiency through a collaborative onshore and offshore support model.
• Timely availability of data for critical reports and analytics, ensuring informed decision-making.
• Improved work-life balance for the client’s IT staff by alleviating the burden of continuous data pipeline management.
• Increased customer satisfaction, reflected in happier end users and their clients.

KEYWORDS
Data Engineering, Business Intelligence, Data Warehousing, Data Warehouse Migration, Data Migration, Data Integration, OLAP

SOFTWARE
Snowflake, Teradata, Python, SUSE Linux, InfoSphere DataStage, Eclipse, UC4

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