Data Platform Engineer at On

📍 Job Overview

Job Title: Data Platform Engineer
Company: On
Location: Zürich, Switzerland
Job Type: On-site
Category: DevOps Engineer
Date Posted: June 24, 2025
Experience Level: Entry-level to Mid-level (0-2 years)
Remote Status: On-site

🚀 Role Summary

Design and implement scalable data platforms to ensure data availability and reliability for analytics and reporting.
Collaborate with data engineers, data scientists, and stakeholders to understand data needs and provide suitable solutions.
Manage and optimize data processing pipelines to improve performance and efficiency.
Monitor and maintain data quality, ensuring data integrity and consistency across platforms.
Stay up-to-date with emerging data technologies and best practices, and incorporate them into the data platform as needed.

📝 Enhancement Note: This role requires a strong background in data engineering and familiarity with data platforms. Experience with cloud technologies and data processing frameworks is highly preferred.

💻 Primary Responsibilities

Data Platform Design & Implementation: Design and implement data platforms tailored to the organization's analytics and reporting needs.
Data Pipeline Management: Manage and optimize data processing pipelines to ensure efficient data flow and minimal latency.
Data Quality Assurance: Monitor and maintain data quality, ensuring data integrity, consistency, and accuracy across platforms.
Stakeholder Collaboration: Work closely with data engineers, data scientists, and stakeholders to understand data requirements and provide suitable solutions.
Emerging Technology Adoption: Stay current with emerging data technologies and best practices, and integrate them into the data platform as needed.

📝 Enhancement Note: This role requires a strong understanding of data processing frameworks, cloud technologies, and data warehousing principles. Experience with big data technologies such as Apache Spark, Hadoop, or Hive is highly preferred.

🎓 Skills & Qualifications

Education: A bachelor's degree in Computer Science, Information Technology, or a related field. Relevant coursework in data engineering, data warehousing, or a similar discipline is preferred.

Experience: At least 0-2 years of experience in data engineering, data warehousing, or a related role. Experience with cloud technologies and data processing frameworks is highly preferred.

Required Skills:

Proficiency in data processing frameworks such as Apache Spark, Hadoop, or Hive.
Strong knowledge of SQL and experience with data warehousing concepts.
Familiarity with cloud technologies such as AWS, GCP, or Azure.
Experience with data pipeline management tools such as Apache Airflow or Luigi.
Strong problem-solving skills and ability to work independently.

Preferred Skills:

Experience with data visualization tools such as Tableau or Power BI.
Familiarity with data governance and data quality management practices.
Knowledge of data modeling and ETL processes.
Experience with containerization technologies such as Docker or Kubernetes.

📝 Enhancement Note: This role requires a strong technical background in data engineering and familiarity with data processing frameworks. Experience with big data technologies and cloud platforms is highly preferred.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A portfolio demonstrating experience with data processing frameworks, data warehousing, and data pipeline management.
Case studies or projects showcasing data platform design and implementation, data quality assurance, and data pipeline optimization.
Examples of collaboration with stakeholders to understand data needs and provide suitable solutions.

Technical Documentation:

Detailed documentation of data processing pipelines, including data sources, transformations, and destinations.
Data dictionary and data lineage documentation to ensure data integrity and consistency.
Documentation of data quality assurance processes and metrics.

📝 Enhancement Note: This role requires a strong technical portfolio demonstrating experience with data processing frameworks, data warehousing, and data pipeline management. Case studies or projects showcasing data platform design and implementation, data quality assurance, and data pipeline optimization are highly preferred.

💵 Compensation & Benefits

Salary Range: The estimated salary range for this role in Zürich, Switzerland is CHF 80,000 - 120,000 per year, based on regional market data and industry standards for data engineers with 0-2 years of experience.

Benefits:

Competitive health, dental, and vision insurance plans.
Retirement savings plans with company matching.
Generous time-off policies, including vacation, sick leave, and parental leave.
Professional development opportunities, including training, workshops, and conference attendance.
A dynamic and collaborative work environment with a focus on innovation and growth.

Working Hours: Full-time position with standard working hours Monday through Friday, 9:00 AM to 5:00 PM CET. Some flexibility for occasional remote work may be available.

📝 Enhancement Note: The estimated salary range is based on regional market data and industry standards for data engineers with 0-2 years of experience. Benefits may vary based on the company's specific benefits package.

🎯 Team & Company Context

🏢 Company Culture

Industry: On is a leading sports technology company that develops innovative running shoes and apps to enhance the running experience. The data platform engineer role is crucial for supporting the company's data-driven decision-making and improving the user experience.

Company Size: On has a team of over 300 employees across multiple locations, including Zürich, Switzerland. As a mid-sized company, On offers a dynamic and collaborative work environment with a strong focus on innovation and growth.

Founded: On was founded in 2010 by Olivier Bernhard, David Allemann, and Caspar Coppetti. The company has since grown to become a global leader in the sports technology industry, with a strong commitment to sustainability and social responsibility.

Team Structure:

The data team at On is responsible for managing and analyzing data to support the company's business decisions and improve the user experience.
The data platform engineer will work closely with data engineers, data scientists, and stakeholders to understand data needs and provide suitable solutions.
The data team is part of the broader technology organization, which includes software engineers, quality assurance engineers, and IT specialists.

Development Methodology:

On follows Agile development methodologies, with a focus on iterative development and continuous improvement.
The data team uses version control systems such as Git to manage code and collaborate on projects.
On employs a data-driven approach to decision-making, with a strong emphasis on data quality, data governance, and data security.

Company Website: On Running

📝 Enhancement Note: On is a mid-sized sports technology company with a strong focus on innovation and growth. The data platform engineer role is crucial for supporting the company's data-driven decision-making and improving the user experience.

📈 Career & Growth Analysis

Web Technology Career Level: The data platform engineer role is an entry-level to mid-level position within the data engineering career path. This role provides an excellent opportunity to gain experience with data processing frameworks, data warehousing, and data pipeline management.

Reporting Structure: The data platform engineer will report directly to the data engineering manager and work closely with data engineers, data scientists, and stakeholders to understand data needs and provide suitable solutions.

Technical Impact: The data platform engineer will have a significant impact on the company's data infrastructure, data quality, and data availability. By designing and implementing scalable data platforms, the data platform engineer will enable the company to make data-driven decisions and improve the user experience.

Growth Opportunities:

Technical Growth: As the data platform engineer gains experience and expertise, they may have the opportunity to take on more complex projects and lead initiatives to improve the company's data infrastructure.
Leadership Growth: With experience and strong performance, the data platform engineer may have the opportunity to move into a leadership role, such as data engineering manager or data architecture manager.
Broad Technical Growth: The data platform engineer may have the opportunity to gain experience with emerging data technologies and expand their technical skillset beyond data engineering.

📝 Enhancement Note: The data platform engineer role is an entry-level to mid-level position within the data engineering career path. This role provides an excellent opportunity to gain experience with data processing frameworks, data warehousing, and data pipeline management, and to make a significant impact on the company's data infrastructure, data quality, and data availability.

🌐 Work Environment

Office Type: On's Zürich office is a modern, collaborative workspace designed to foster innovation and creativity. The office features an open floor plan, plenty of natural light, and state-of-the-art technology.

Office Location(s): On's Zürich office is located in the vibrant Seefeld district, with easy access to public transportation and numerous amenities.

Workspace Context:

Collaboration: On encourages a collaborative work environment, with regular team meetings, workshops, and social events.
Technology: On provides state-of-the-art technology, including high-performance workstations, multiple monitors, and the latest software tools.
Flexibility: On offers some flexibility for occasional remote work, with a focus on results and productivity.

Work Schedule: On offers a flexible work schedule, with standard working hours Monday through Friday, 9:00 AM to 5:00 PM CET. Some flexibility for occasional remote work may be available.

📝 Enhancement Note: On's Zürich office is a modern, collaborative workspace designed to foster innovation and creativity. The office features an open floor plan, plenty of natural light, and state-of-the-art technology. On encourages a collaborative work environment, with regular team meetings, workshops, and social events.

📄 Application & Technical Interview Process

Interview Process:

Technical Assessment (1 hour): A hands-on technical assessment to evaluate the candidate's data engineering skills, including data processing frameworks, data warehousing, and data pipeline management.
Cultural Fit Interview (30 minutes): A conversation to assess the candidate's cultural fit with On's values and work environment.
Final Interview (30 minutes): A final interview with the data engineering manager to discuss the candidate's fit for the role and the team.

Portfolio Review Tips:

Data Processing Projects: Highlight projects that demonstrate experience with data processing frameworks, data warehousing, and data pipeline management.
Data Quality Assurance: Showcase examples of data quality assurance processes and metrics.
Stakeholder Collaboration: Provide case studies or projects that demonstrate experience working with stakeholders to understand data needs and provide suitable solutions.

Technical Challenge Preparation:

Data Processing Frameworks: Brush up on your knowledge of Apache Spark, Hadoop, or Hive, and be prepared to discuss their use cases and best practices.
Data Warehousing: Review data warehousing concepts, including data modeling, ETL processes, and data pipeline optimization.
Cloud Technologies: Familiarize yourself with cloud technologies such as AWS, GCP, or Azure, and be prepared to discuss their use cases and best practices.

ATS Keywords: [Data Engineering, Data Warehousing, Data Pipeline, Apache Spark, Hadoop, Hive, AWS, GCP, Azure, Data Quality, Data Governance, Data Processing, Data Pipeline Management]

📝 Enhancement Note: The interview process for the data platform engineer role at On includes a technical assessment, a cultural fit interview, and a final interview. The technical assessment focuses on data engineering skills, including data processing frameworks, data warehousing, and data pipeline management. The cultural fit interview assesses the candidate's fit with On's values and work environment.

🛠 Technology Stack & Web Infrastructure

Data Processing Frameworks:

Apache Spark
Hadoop
Hive

Cloud Technologies:

AWS
GCP
Azure

Data Warehousing:

Amazon Redshift
Google BigQuery
Azure Synapse Analytics

Data Pipeline Management:

Apache Airflow
Luigi
Prefect

Data Visualization:

Tableau
Power BI
Looker

📝 Enhancement Note: The technology stack for the data platform engineer role at On includes data processing frameworks such as Apache Spark, Hadoop, and Hive. The role also requires familiarity with cloud technologies such as AWS, GCP, and Azure, as well as data warehousing tools such as Amazon Redshift, Google BigQuery, and Azure Synapse Analytics.

👥 Team Culture & Values

Data Team Values:

Data-Driven Decision Making: On values a data-driven approach to decision-making and uses data to inform business strategies and improve the user experience.
Collaboration: On encourages collaboration and cross-functional teamwork to ensure that data is used effectively across the organization.
Innovation: On fosters a culture of innovation and encourages team members to explore new technologies and approaches to data management.
Continuous Learning: On values continuous learning and provides opportunities for team members to develop their skills and advance their careers.

Collaboration Style:

Cross-Functional Collaboration: On encourages collaboration between data teams, software engineers, quality assurance engineers, and IT specialists to ensure that data is used effectively across the organization.
Data-Driven Decision Making: On values a data-driven approach to decision-making and uses data to inform business strategies and improve the user experience.
Regular Communication: On fosters a culture of regular communication and encourages team members to share their ideas and insights with the broader organization.

📝 Enhancement Note: On values a data-driven approach to decision-making and encourages collaboration and cross-functional teamwork to ensure that data is used effectively across the organization. The data team at On fosters a culture of innovation and continuous learning, with a strong emphasis on exploring new technologies and approaches to data management.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Data Scalability: Design and implement scalable data platforms to support the company's growing data needs.
Data Latency: Optimize data processing pipelines to minimize latency and ensure real-time data availability.
Data Quality: Monitor and maintain data quality, ensuring data integrity, consistency, and accuracy across platforms.
Emerging Technologies: Stay up-to-date with emerging data technologies and best practices, and incorporate them into the data platform as needed.

Learning & Development Opportunities:

Technical Training: On provides opportunities for team members to develop their technical skills through training, workshops, and conference attendance.
Mentorship: On offers mentorship opportunities to help team members advance their careers and gain experience with emerging technologies.
Leadership Development: On provides opportunities for team members to develop their leadership skills and take on more complex projects.

📝 Enhancement Note: The data platform engineer role at On presents several technical challenges, including data scalability, data latency, data quality, and emerging technologies. The role also offers numerous learning and development opportunities, including technical training, mentorship, and leadership development.

💡 Interview Preparation

Technical Questions:

Data Processing Frameworks: Be prepared to discuss your experience with data processing frameworks such as Apache Spark, Hadoop, or Hive, and their use cases and best practices.
Data Warehousing: Review data warehousing concepts, including data modeling, ETL processes, and data pipeline optimization, and be prepared to discuss their application in a real-world scenario.
Cloud Technologies: Familiarize yourself with cloud technologies such as AWS, GCP, or Azure, and be prepared to discuss their use cases and best practices.

Company & Culture Questions:

Data-Driven Decision Making: Be prepared to discuss your experience with data-driven decision-making and how you have used data to inform business strategies and improve the user experience.
Collaboration: Be prepared to discuss your experience with collaboration and cross-functional teamwork, and how you have used data to drive results across the organization.
Innovation: Be prepared to discuss your experience with innovation and how you have explored new technologies and approaches to data management.

Portfolio Presentation Strategy:

Data Processing Projects: Highlight projects that demonstrate experience with data processing frameworks, data warehousing, and data pipeline management.
Data Quality Assurance: Showcase examples of data quality assurance processes and metrics.
Stakeholder Collaboration: Provide case studies or projects that demonstrate experience working with stakeholders to understand data needs and provide suitable solutions.

📝 Enhancement Note: The interview process for the data platform engineer role at On includes technical questions focused on data processing frameworks, data warehousing, and cloud technologies. The interview also includes company and culture questions focused on data-driven decision-making, collaboration, and innovation.

📌 Application Steps

To apply for this data platform engineer position at On:

Submit your application through the application link provided.
Customize your resume and portfolio to highlight your relevant experience with data processing frameworks, data warehousing, and data pipeline management.
Prepare for the technical assessment by reviewing your knowledge of data processing frameworks, data warehousing, and cloud technologies.
Research On's data-driven approach to decision-making and be prepared to discuss your experience with data-driven decision-making and collaboration in your interview.
Prepare for the cultural fit interview by reviewing On's values and work environment, and be prepared to discuss your fit with the company's culture and work environment.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and data engineering industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Data Platform Engineer