Data Engineer

Planck Technologies; Lisboa; Hybrid; EUR 45,000 - 75,000; Full time

Benefits offered by this job

Hybrid work model

International environment

Job summary

Planck Technologies is a Lisbon-based software company seeking a data engineer to design and maintain production-grade ETL/ELT pipelines, integrating data from APIs, file-based sources, and cloud services. You will craft clean data products across operational, logistics, sales, and customer data, with emphasis on quality, governance, and scalability.

You will collaborate with data scientists, AI engineers, and business stakeholders across international teams, using GenAI tooling to accelerate

Qualifications

• Strong experience with Python, SQL, data modelling, and ETL/ELT development.
• Proven experience in designing, developing, and maintaining production-ready data pipelines.
• Experience with cloud-native data engineering, preferably within Azure environments.
• Practical exposure to AWS data services is a plus.
• Experience with Snowflake or comparable cloud data platforms.
• Solid understanding of data quality, data orchestration, observability, data lineage, metadata management, and data governance.
• Ability to transform data from multiple sources into clean, documented, reusable, and reliable data products.
• Experience integrating data from APIs, file-based sources, operational systems, and cloud services.
• Familiarity with data protection, access management, and enterprise data governance requirements.
• Ability to collaborate with data scientists, AI engineers, business stakeholders, and local IT teams in international environments.
• Comfortable using GenAI software engineering tools such as GitHub Copilot, Cursor, or OpenCode to accelerate development.
• Strong analytical, problem-solving, and communication skills.

Responsibilities

• Design, build, and operate enterprise-grade ETL and ELT pipelines to support AI, analytics, reporting, and agentic workflow use cases.
• Develop clean, documented, reusable data products from operational, customer, logistics, sales, and other enterprise data sources.
• Implement data quality checks, monitoring, data lineage, metadata management, and reliability mechanisms to ensure datasets are trustworthy and production-ready.
• Support integrations between Azure-based AI services, Snowflake, APIs, file-based sources, and selected AWS data services.
• Translate business and AI requirements into effective data models, pipelines, and data-serving layers.
• Collaborate with data scientists, AI engineers, business stakeholders, and local IT teams to deliver scalable and reliable data solutions.
• Work closely with data owners and IT teams to onboard new data sources while ensuring compliance with data protection, access control, and governance requirements.
• Leverage modern data engineering practices and GenAI development tools to improve productivity and accelerate delivery.
• Contribute to the development of scalable and reusable data foundations that enable AI teams to move from experimentation to production.
• Help ensure the availability of reliable, well-governed datasets to support AI initiatives across different countries.

Skills

Python

SQL

ETL/ELT

Data pipelines

Azure

Snowflake

Data governance

GenAI tools

Data quality

APIs

Tools

GitHub Copilot

Cursor

OpenCode

Job description

Planck Technologies is a company specialized in Software Development, dedicated to shaping futures and creating value through innovative IT solutions. By expanding teams and offering a comprehensive range of services from Software Development and Infrastructure Management to Cybersecurity we empower clients with all the expertise they need in one place. Inspired by the principles of quantum physics, we push beyond traditional boundaries to deliver customized solutions that redefine the IT landscape and drive shared success.

What are we looking for?

• Strong experience with Python, SQL, data modelling, and ETL/ELT development.
• Proven experience in designing, developing, and maintaining production-ready data pipelines.
• Experience with cloud-native data engineering, preferably within Azure environments.
• Practical exposure to AWS data services is a plus.
• Experience with Snowflake or comparable cloud data platforms.
• Solid understanding of data quality, data orchestration, observability, data lineage, metadata management, and data governance.
• Ability to transform data from multiple sources into clean, documented, reusable, and reliable data products.
• Experience integrating data from APIs, file-based sources, operational systems, and cloud services.
• Familiarity with data protection, access management, and enterprise data governance requirements.
• Ability to collaborate with data scientists, AI engineers, business stakeholders, and local IT teams in international environments.
• Comfortable using GenAI software engineering tools such as GitHub Copilot, Cursor, or OpenCode to accelerate development.
• Strong analytical, problem-solving, and communication skills.

Nice to Have

• Experience with Apache Iceberg, AWS Glue, Spark, Databricks, or similar data engineering technologies.
• Experience working with logistics, parcel operations, customer feedback, route, depot, or sales data.
• Exposure to AI feature stores, vector databases, retrieval pipelines, or data preparation for Generative AI applications.
• Experience supporting AI, analytics, reporting, or agentic workflow use cases in enterprise environments.

What will you do?

• Design, build, and operate enterprise-grade ETL and ELT pipelines to support AI, analytics, reporting, and agentic workflow use cases.
• Develop clean, documented, reusable data products from operational, customer, logistics, sales, and other enterprise data sources.
• Implement data quality checks, monitoring, data lineage, metadata management, and reliability mechanisms to ensure datasets are trustworthy and production-ready.
• Support integrations between Azure-based AI services, Snowflake, APIs, file-based sources, and selected AWS data services.
• Translate business and AI requirements into effective data models, pipelines, and data-serving layers.
• Collaborate with data scientists, AI engineers, business stakeholders, and local IT teams to deliver scalable and reliable data solutions.
• Work closely with data owners and IT teams to onboard new data sources while ensuring compliance with data protection, access control, and governance requirements.
• Leverage modern data engineering practices and GenAI development tools to improve productivity and accelerate delivery.
• Contribute to the development of scalable and reusable data foundations that enable AI teams to move from experimentation to production.
• Help ensure the availability of reliable, well-governed datasets to support AI initiatives across different countries.

Location: Lisbon

Working Model: Hybrid (2 days per week in the office)