Data Engineer

Data Engineer (Databricks, Python, Spark) At Hays, we are partnering with a leading global company in the financial services and investment management sector to find an experienced Data Engineer to join a strategic data transformation project.This is an exciting opportunity to work with international teams, modern cloud technologies, and large-scale financial datasets, helping build and enhance a key data platform that supports critical investment operations and analytics.What you will doDesign, develop, and maintain scalable data pipelines using Databricks.Build Big Data solutions with Spark, PySpark, and cloud technologies.Collaborate with global investment and technology teams to deliver data-driven solutions.Support business stakeholders with data insights and reporting.Contribute to best practices, CI/CD processes, and continuous improvement initiatives.Explore and leverage AI and LLM technologies to enhance platform capabilities.What we are looking for3+ years of Data Engineering experience including 2+ years designing and building Databricks data pipelines is required.2+ years of hands-on Python/Pyspark/SparkSQL and/or Scala experience2+ years of experience with Big Data pipelines or DAG Tools (Data Factory, Airflow, dbt, or similar)2+ years of Spark experience (especially Databricks Spark and Delta Lake)2+ years of hands-on experience implementing Big Data solutions in a cloud ecosystem, including Data/Delta LakesAzure cloud is highly preferred, however will consider AWS, GCP or other cloud platform experience in lieu.Experience with AI, LLMs, Databricks Genie or similar tools is required2+ years of SQL experience, specifically to write complex, highly optimized queries across large volumes of data is highly desiredExperience working in a controlled environment with Github and CI/CD release pipelines is highly desiredExperience with financial data providers such as Bloomberg in an asset/investment management firm or other financial service company is a plus.Experience with conceptual, logical and/or physical database designs is a plusMust be comfortable speaking EnglishA positive attitude and a ‘can do’ mentality are essentialWhat We OfferHybrid and flexible working model: only 1 day per week on-site at the Barcelona office (Via Augusta)Opportunity to join a high-impact global data transformation program within a leading international financial services organization If you’re passionate about building impactful solutions and want to be part of a collaborative, forward-thinking team, we’d love to hear from you.

experience including 2+ years designing and building Databricks data pipelines is required.2+ years of hands-on Python/Pyspark/SparkSQL and/or Scala experience2+ years of experience with Big Data pipelines or DAG Tools (Data Factory, Airflow, dbt, or similar)2+ years of Spark experience (especially Databricks Spark and Delta Lake)2+ years of hands-on experience implementing Big Data solutions in a cloud ecosystem, including Data/Delta LakesAzure cloud is highly preferred, however will consider AWS, GCP or other cloud platform experience in lieu.Experience with AI, LLMs, Databricks Genie or similar tools is required2+ years of SQL experience, specifically to write complex, highly optimized queries across large volumes of data is highly desiredExperience working in a controlled environment with Github and CI/CD release pipelines is highly desiredExperience with financial data providers such as Bloomberg in an asset/investment management firm or other financial service company is a plus.Experience with conceptual, logical and/or physical database designs is a plusMust be comfortable speaking EnglishA positive attitude and a ‘can do’ mentality are essentialWhat We OfferHybrid and flexible working model: only 1 day per week on-site at the Barcelona office (Via Augusta)Opportunity to join a high-impact global data transformation program within a leading international financial services organization If you’re passionate about building impactful solutions and want to be part of a collaborative, forward-thinking team, we’d love to hear from you.