Senior Data Engineer

Parfois; Porto; Híbrido; EUR 70 000 - 90 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Hybrid work model

Innovation and continuous improvement

Career development

Resumo da oferta

PARFOIS Data Intelligence is searching for a Senior/Lead Data Engineer to drive the evolution of our Azure/Snowflake-based data platform. You will design scalable data pipelines and mentor the team, shaping data governance and best practices across a global retail organization.

You will lead architectural decisions, implement CI/CD for data components and collaborate with analytics, IT and business stakeholders to enable data‑driven decision making.

Qualificações

• 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 optimise data processes.
• Ability to define technical standards and software engineering best practices.
• Strong communication skills and ability to engage with different stakeholders.

Responsabilidades

• 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.

Conhecimentos

Data Engineering

Technical leadership

SQL

ETL/ELT

Azure

Snowflake

Data warehousing

Airflow

Azure Data Factory

Git

CI/CD

Communication

Problem solving

Ferramentas

Airflow

Azure Data Factory

Git

Azure DevOps

Descrição da oferta de emprego

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.

MAIN RESPONSIBILITIES:

• 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.

REQUIREMENTS (MUST HAVE):

• 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 optimise 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.

WHAT WE OFFER:

• 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.