Solid experience in Data Engineering, ideally 5+ years.
Experience in technical leadership, architecture definition and team guidance.
Strong knowledge of SQL and relational databases, particularly SQL Server.
Solid experience developing ETL/ELT solutions and data pipelines.
Experience with Cloud Data Platforms, preferably Microsoft Azure.
Experience with Snowflake or other Cloud Data Warehouse platforms.
Knowledge of Data Warehouse, Data Lake and Data Platform architectures.
Experience with orchestration tools such as Airflow and/or Azure Data Factory.
Experience with Git and CI/CD practices, ideally Azure DevOps.
Ability to analyse complex problems, identify bottlenecks and optimize data processes.
Ability to define technical standards and software engineering best practices.
Strong communication skills and ability to engage with different stakeholders.

Nice to have

Knowledge of Python and SQL applied to Data Engineering.
Experience with Kafka, Streaming and Event-Driven Architectures.
Experience with Azure Functions, Azure Blob Storage or other Azure services.
Experience with Power BI, SSAS and integration between Data Engineering and BI platforms.
Knowledge of Data Governance, Data Quality and Data Observability.
Experience migrating data platforms from On-Premise to Cloud environments.
Knowledge of modern data architectures, namely Lakehouse and distributed processing technologies.
Experience working in Agile/Scrum environments and backlog management tools.

Pay & benefits

What you'll get

The opportunity to shape and influence the evolution of a modern enterprise data platform.
Participation in high-impact projects within a global retail organization.
Exposure to a modern technology stack, including Azure, Snowflake and advanced Data Engineering practices.
Integration into a highly skilled and collaborative Data Intelligence team.
Continuous learning and professional development opportunities.
Autonomy to influence architectural decisions and technical direction.
Hybrid working model and a strong focus on innovation and continuous improvement.

The role

Join PARFOIS Data Intelligence and play a key role in shaping the future of our global data platform.
We are looking for a Senior / Lead Data Engineer to drive the evolution of our Data Engineering capabilities, combining hands-on engineering, technical leadership, architecture design and data platform modernization.
You will be responsible for designing and implementing scalable, reliable and secure data solutions that support analytics, operational reporting, advanced data initiatives and business decision-making across a global retail organization.
This role will serve as the technical reference for the Data Engineering area, driving architectural decisions, establishing best practices and supporting the technical development of the team, while ensuring the continuous evolution of our Azure and Snowflake-based data ecosystem.

Join the PARFOIS Data Intelligence team and take on a fundamental role in building the future of our global data platform.

We are looking for a Senior / Lead Data Engineer to lead the evolution of our Data Engineering capabilities, combining hands-on technical development, technical leadership, architecture definition and data platform modernization.

You will be responsible for designing and implementing scalable, reliable and secure data solutions that support analytics, operational reporting, advanced data initiatives and decision-making in a global retail organization.

This role will be the main technical reference for the Data Engineering area, driving architecture decisions, defining best practices and supporting the technical development of the team, while simultaneously ensuring the continuous evolution of our data ecosystem based on Azure and Snowflake.

What you'll do

Act as the technical reference for the Data Engineering team, promoting best practices, technical excellence and knowledge sharing.
Define and evolve the organization's data architecture, ensuring scalability, performance, security and maintainability.
Design, develop and maintain robust ETL/ELT processes and data pipelines across the enterprise data platform.
Develop scalable data products that support reporting, analytics and advanced data initiatives.
Lead the evolution of our Cloud Data Platform, with a strong focus on Azure and Snowflake technologies.
Integrate data from multiple sources, including databases, APIs, SaaS platforms, files and streaming technologies.
Optimize existing processes and data pipelines, improving performance, reliability and operational efficiency.
Implement monitoring, observability, logging, auditing and data quality frameworks.
Define development standards, architecture guidelines, version control practices and engineering processes.
Evaluate and define technical solutions, balancing performance, scalability, cost and maintainability.
Lead the implementation and evolution of CI/CD practices and deployment processes for Data Engineering components.
Collaborate closely with Data Analytics teams, business areas, IT and other stakeholders to support data-driven decision-making.
Support the technical growth of team members through mentoring, coaching and technical guidance.
Contribute to the long-term Data Engineering roadmap, helping modernize the platform towards cloud-native and scalable architectures.
Promote Data Governance principles and ensure data quality, consistency and trustworthiness across the platform.

MAIN RESPONSIBILITIES

Be the technical reference for the Data Engineering team, promoting best practices, technical excellence and knowledge sharing.
Define and evolve the organization's data architecture, ensuring scalability, performance, security and maintainability.
Design, develop and maintain robust ETL/ELT processes and data pipelines across the enterprise data platform.
Develop scalable data products that support reporting, analytics and advanced data initiatives.
Lead the evolution of our Cloud Data Platform, with a focus on Azure and Snowflake technologies.
Integrate data from multiple sources, including databases, APIs, SaaS platforms, files and streaming technologies.
Optimize existing processes and pipelines, improving performance, reliability and operational efficiency.
Implement monitoring, observability, logging, auditing and data quality control mechanisms.
Define development standards, architecture guidelines, versioning practices and engineering processes.
Evaluate and define technical solutions, balancing performance, scalability, cost and maintainability.
Lead the implementation and evolution of CI/CD practices and deployment processes for Data Engineering components.
Collaborate closely with Data Analytics teams, business areas, IT and other stakeholders to support data-driven decisions.
Support the technical growth of team members through mentoring, coaching and technical guidance.
Contribute to the long-term roadmap of the Data Engineering area, helping to modernize the platform for cloud-native and scalable architectures.
Promote Data Governance principles and ensure data quality, consistency and reliability across the platform.

REQUIREMENTS (MUST HAVE)

Solid experience in Data Engineering, ideally 5+ years.
Technical leadership / architecture definition and team guidance experience.
Strong knowledge of SQL and relational databases, namely SQL Server.
Solid experience in ETL/ELT and data pipeline development.
Experience with Cloud Data Platforms, preferably Azure.
Experience with Snowflake or other Cloud Data Warehouse platforms.
Knowledge of Data Warehouse / Data Lake / Data Platform architecture.
Experience with orchestration tools, such as Airflow or Azure Data Factory.
Experience with Git and CI/CD, ideally Azure DevOps.
Ability to analyze complex problems, identify bottlenecks and optimize data processes.
Ability to define technical standards and development best practices.
Good communication and interaction skills with different stakeholders.

WE VALUE (NICE TO HAVE)

Knowledge of Python and SQL applied to Data Engineering.
Experience with Kafka / streaming / event-driven architectures.
Experience with Azure Functions, Blob Storage or other Azure services.
Experience with Power BI / SSAS and integration between Data Engineering and BI platforms.
Knowledge of Data Governance, Data Quality and Data Observability.
Experience in on-premise to Cloud platform migrations.
Knowledge of modern data architectures, namely Lakehouse and distributed processing.
Experience in Agile/Scrum methodologies and backlog management tools.

WHAT WE OFFER

Opportunity to influence and shape the evolution of a modern enterprise data platform.
Participation in high-impact projects within a global retail organization.
Working with a modern technology stack, including Azure, Snowflake, and advanced Data Engineering practices.
Integration into a highly skilled and collaborative Data Intelligence team.
Continuous opportunities for learning and professional development.
Autonomy to influence architecture decisions and technical direction.
Hybrid working model and a strong commitment to innovation and continuous improvement.