Lead Data Engineer — Cloud Platform (Azure/Snowflake)

Parfois

Porto

Híbrido

EUR 70 000 - 90 000

Tempo integral

há 10 horas
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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

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.

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