Senior Data Platform Engineer

INTERSPORT Group; Bern; Vor Ort; CHF 140.000 - 190.000; Vollzeit

Zusammenfassung

INTERSPORT Group is seeking a Senior Platform Data Engineer to design, build and operate the Group Data Platform and trusted data products powering reporting, analytics and AI across the business.

You will transform Sell-Out, Sell-In and Stock data into actionable insights, collaborate with analysts and stakeholders, and contribute to SAP modernisation and Copilot adoption while ensuring data accessibility and governance.

Qualifikationen

• Degree or equivalent professional experience in computer science or data engineering.
• 5–8 years of data engineering or data-platform experience.
• Experience with production data pipelines across lifecycle.
• Experience with cross-functional teams and SAP/retail context advantageous.

Aufgaben

• Design, build and operate scalable data platform solutions in Microsoft Fabric and Azure.
• Build reusable ETL/ELT pipelines using Python, PySpark and SQL across SAP, databases, APIs and file‑based data.
• Deliver governed data products for Sell‑Out, Stock and Sell‑In reporting, analytics and Copilot.
• Maintain performance, storage and monitoring with robust release controls.

Kenntnisse

Data engineering

ETL/ELT pipelines

Cross-functional collaboration

Problem-solving

Mentoring / leadership

Ausbildung

Bachelor's degree in Computer Science or related field

Tools

Python

PySpark

SQL

Microsoft Fabric

Azure data services

OneLake

Power BI

Git

CI/CD

Jobbeschreibung

Do you want to work at the Heart of Sport, at the pulse of the global sporting goods industry, with the leading sport brands in the world? Then you need to join INTERSPORT.

We are looking for a motivated and detail-oriented Senior Platform Data Engineer to join our growing data team.

ABOUT THE ROLE

As a Senior Data Platform Engineer, you will design, build and operate INTERSPORT's Group Data Platform and trusted retail data products that power reporting, analytics and AI across the business. Working closely with data analysts and business stakeholders, you will develop scalable, reliable and governed data pipelines that transform Sell-Out, Sell-In and Stock data into actionable insights, enabling better decision-making and more efficient commercial operations. You will play a key role in ensuring data is accessible, trusted and fit for purpose while supporting SAP modernisation and the responsible adoption of Microsoft Copilot and generative AI.

RESPONSIBILTIES

• Design, build, test and operate scalable solutions in Microsoft Fabric and Azure, including OneLake, lakehouses, warehouses, notebooks, semantic models and pipelines.
• Build reusable ETL/ELT pipelines and transformations using Python, PySpark and SQL across SAP, databases, APIs, files and external-provider data.
• Deliver governed data products for Group Sell-Out, Stock and Sell-In reporting, SAP decommissioning, analytics, AI and Copilot.
• Optimise performance, storage and capacity, with effective monitoring, alerting, recovery, testing and release controls.

Data quality, governance and operational ownership

• Embed automated quality controls, reconciliation and observability; resolve incidents through root‑cause analysis and corrective action.
• Maintain lineage, metadata, data contracts, source‑to‑target rules, business definitions, ownership, access controls and technical documentation.
• Own production reliability, runbooks and support procedures, balancing delivery speed with resilience, security, privacy and maintainability.

Technical leadership and collaboration

• Translate business needs into clear technical scope, outcomes and acceptance criteria; communicate complexity, options, dependencies, risks and trade‑offs in accessible language.
• Collaborate closely with analysts, business owners, IT teams, National Organisations and partners, building trust across cultures and functions and creating shared accountability for outcomes.
• Facilitate decisions, workshops and design reviews; listen actively, challenge constructively, manage expectations and facilitate a collaborative culture.
• Set engineering standards, review architecture and code, mentor colleagues and promote documentation, knowledge sharing and continuous improvement.

Partner and vendor management

• Act as the internal technical counterpart for partners, challenging solutions constructively and managing acceptance criteria, testing, defects, deployment and sign‑off.
• Ensure solutions are robust, documented, transferable and internally supportable, with effective knowledge transfer and reduced dependency on individual external resources.
• Review and challenge proposed solutions to ensure alignment with business and technical requirements.
• Ensure documentation, knowledge transfer and operational handover are completed to agreed standards.

KEY REQUIREMENTS

Technical expertise

• Strong hands‑on experience with Microsoft Fabric, Azure data services, Python, PySpark, SQL and production ETL/ELT pipelines.
• Practical knowledge of lakehouse and warehouse architecture, dimensional modelling, OneLake, semantic models and Power BI dependencies.
• Experience integrating SAP, relational databases, APIs and file‑based data, including XML, JSON, CSV and Parquet.
• Sound engineering practices covering Git, CI/CD, automated testing, environment management, monitoring, security, data quality and governance.
• Ability to troubleshoot complex data, pipeline, performance and connectivity issues and to design maintainable, metadata‑driven solutions.
• Knowledge of the foundations for trustworthy AI: high‑quality data, metadata, semantic models, business definitions, lineage, access controls, privacy and responsible‑use principles.

Communication, collaboration and ways of working

• Clear, concise communicator who adapts technical content for business, executive and engineering audiences.
• Strong collaborator who can align diverse stakeholders, facilitate decisions, influence without authority and work effectively across international and cross‑functional teams.
• Constructive challenger with strong listening, negotiation and conflict‑resolution skills; able to give and receive feedback openly.
• Disciplined in documentation and knowledge transfer, with a service mindset and commitment to developing others.
• Structured, pragmatic and evidence‑led, balancing target architecture with business priorities across concurrent strategic initiatives.
• Hands‑on and accountable, comfortable moving between architecture, implementation and production support.
• Proactive in identifying risks, dependencies and improvement opportunities, and decisive when priorities or requirements are unclear.

QUALIFICATIONS

• Degree in Computer Science, Data Engineering, Information Systems, Software Engineering or a related field, or equivalent relevant professional experience.

PROFESSIONAL EXPERIENCE

• Five to eight years progressive experience in data engineering, data‑platform development or a closely related technical role.
• Proven ownership of production data pipelines and platforms across the full lifecycle, from requirements and architecture to deployment and support.
• Experience in a multi‑stakeholder environment involving analysts, business users, IT specialists and external providers.
• Experience in retail, consumer goods, international organisations or SAP transformation is advantageous.