Data & AI Engineering Specialist

Location: Hybrid role in Barcelona, Spain

Salary: €49,600–€74,400 annually

Company

hirify.global is a global biopharmaceutical company developing medicines and vaccines and using digital, data, and AI capabilities to improve patient outcomes.

What you will do

• Design technical solutions that meet business and engineering requirements.
• Build, optimize, test, validate, and maintain data and ETL pipelines for production use.
• Organize complex data assets and make high-value datasets available to data scientists, analysts, and business users.
• Partner with business and product teams to translate needs into technical solutions.
• Promote code management, automated testing, CI/CD, deployment, data quality, security, and governance practices.
• Coach data engineers, conduct peer reviews, document solutions, and contribute to engineering standards and frameworks.

Requirements

• 6+ years of experience in data engineering, integration, data warehousing, or business intelligence, including cross-functional work on complex data architecture challenges.
• Experience with Spark and Scala, Informatica/IICS or dbt, Python, shell scripting, strong SQL, relational databases, data modeling, and query tuning.
• Familiarity with cloud data platforms, batch, near real-time, and streaming ingestion, and data warehouse concepts.
• Bachelor's degree or equivalent in Computer Science, Engineering, or a related field.
• Pragmatic problem-solving, technical intuition, attention to detail, clear communication, teamwork, and the ability to work in a fast-paced agile environment.
• Fluent English required.

Nice to have

• Life sciences or pharmaceutical experience.
• Snowflake, Airflow, GitHub, and data mesh experience.
• Additional languages.

Culture & Benefits

• Work in cross-functional AI Factory pods on globally deployed data and AI products.
• Contribute to medicines and vaccines reaching patients faster through mission-driven data engineering.
• Access mentorship from leaders and academics in machine learning.
• Continuous learning and opportunities to shape industry-leading engineering practices.
• Inclusive, collaborative environment focused on innovation, sustainability, and social responsibility.