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Project Duration
5 months
Client Industry
Fashion and lifestyle
Target Markets
North America

KUHL

Scalable Data Pipeline for Enhanced Global Product Distribution and Sales Analytics

Technology Stack

No items found.

Client Overview

Challenges in Modeling Subscription and Business Metrics

KÜHL is a premium outdoor apparel brand known for its high-performance clothing designed for adventure and active lifestyles. With a strong focus on innovation and quality, KÜHL offers a wide range of gear tailored for hiking, climbing, and outdoor exploration. The company emphasizes sustainable practices and cutting-edge design to deliver durable, stylish, and functional apparel to outdoor enthusiasts worldwide.

Solutions Delivered

Fully automated, scalable pipelines using GCP tools

Centralized BigQuery warehouse for unified analytics

Modular data architecture supports agile experimentation

Cross-channel insights delivered in near real-time

Team Composition

Data Engineer


Cloud Architect (GCP Specialist)


Data Analyst / BI Developer


Project Manager / Scrum Master

Engagement Type

Full Time Contract

Key Challenges

Key Barriers to Scalable and Unified Data Insights

Inefficient Data Management

Manual data handling slowed down insight generation

Limited Scalability

Existing systems couldn’t handle growth in data volume or complexity


Fragmented Analytics

No unified platform to support cross-channel analysis


Strategic Roadmap

To enable faster, more flexible product insights, we need to design a robust and scalable data pipeline that automates ingestion, transformation, and analysis across sources. Leveraging orchestration tools will minimize manual effort and improve reliability. The new architecture must support agile experimentation, allowing teams to iterate quickly and validate marketing strategies with confidence. Ultimately, this system should empower cross-functional teams with timely, accurate, and actionable data at scale.

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To support scalable and efficient product analytics, we implemented an automated data pipeline using Google Cloud’s modern tooling. DAGs were orchestrated through Cloud Composer to automate ETL workflows, while Cloud Functions provided modular execution and event-driven triggers for dynamic tasks.

Execution Approach

Centralized Job Data Infrastructure and Analytics

To support scalable and efficient product analytics, we implemented an automated data pipeline using Google Cloud’s modern tooling. DAGs were orchestrated through Cloud Composer to automate ETL workflows, while Cloud Functions provided modular execution and event-driven triggers for dynamic tasks.
All ingested data was stored and modeled in BigQuery, ensuring high-performance querying and scalability. SQL-based transformations were managed using Dataform, enabling structured, version-controlled modeling and seamless deployment.
This architecture not only reduced manual effort but also improved agility, allowing marketing and product teams to gain rapid, reliable insights for faster experimentation and decision-making.

BUSINESS IMPACT

Faster Decision Cycles
Higher Marketing ROI
Empowered Business Teams
Scalable Analytics Operations

50,000+

Centralized job listings, boosting visibility across platforms.

15+

Hours saved weekly by automating data processing tasks.

25%

Faster recruiter response time with real-time data access.