Data Engineer

Location: Hybrid, Lisbon, Portugal

Company

hirify.global Re & ILS underwrites reinsurance risks globally and develops analytics and systems to support product-led underwriting and client service.

What you will do

• Support the build and maintenance of data pipelines and data products across the data lifecycle.
• Implement defined data engineering tasks and help deliver accurate, accessible data for analytics and reporting.
• Develop with Python, SQL, dbt, orchestration tools, Snowflake, Azure, and GCP under senior guidance.
• Monitor and troubleshoot pipelines, support validation and lineage, and contribute to documentation.
• Collaborate with Data Engineers, Data Scientists, Product Owners, and business stakeholders in an agile environment.
• Support cost-efficient, performant, and scalable data solutions, including proofs of concept and experimentation.

Requirements

• Some experience or exposure to Data Engineering, software development, or analytics, typically 1–2 years.
• Working knowledge of SQL and basic programming experience, ideally in Python.
• Awareness of cloud platforms such as Azure or GCP and data warehouse technologies such as Snowflake.
• Basic understanding of data modelling concepts, including OLTP and OLAP, and familiarity with structured and semi-structured data.
• Exposure to SQL databases.
• Familiarity with source control and an interest in CI/CD practices and automated testing.
• Ownership of assigned tasks through completion, pragmatic problem-solving, and interest in agile environments.
• Strong communication skills, cross-functional collaboration, and openness to feedback and coaching.

Nice to have

• Degree in Computer Science, Engineering, Mathematics, Data Science, or a related field.
• Exposure to orchestration tools such as Prefect, Airflow, or ADF, and dbt or similar transformation tools.
• Awareness of data governance concepts.
• Exposure to BI tools such as Power BI or Dataiku.
• Exposure to legacy tools such as SQL Server or SSIS.

Culture & Benefits

• Permanent, full-time employment in a hybrid workplace.
• Work within a collaborative Data Engineering Chapter alongside Data Engineers, Data Scientists, Product Owners, and business stakeholders.
• Receive guidance, coaching, code review feedback, and opportunities for knowledge sharing.
• Contribute to secure, reliable, cost-effective data solutions across cloud and on-premises platforms.