Senior Data Engineer – Hadoop & Spark

Our client is a leading financial institution focused on enterprise analytics in a fast-paced environment.

• At least 7 years of enterprise experience in data engineering or related roles.
• Extensive proficiency with Hadoop, Spark, and Splunk.
• Strong skills in data modelling and designing ETL pipelines using Python.
• Hands-on experience working with SQL and NoSQL data systems.
• Knowledge of CI/CD processes and source control tools.
• Proven leadership skills in technical environments, with a track record of mentoring teams.
• Excellent stakeholder management and communication capabilities.
• Solid understanding of risk management and compliance standards in enterprise settings.

Nice to have:

• Experience with agile methodologies such as Scrum or Kanban.
• Engagement with industry groups or external vendors.

• Design and implement enterprise-grade data pipelines using Hadoop, Spark, and Splunk.
• Build and optimise ETL/ELT workflows leveraging Python for efficient data processing.
• Manage raw, structured, semi-structured, and unstructured data across SQL and NoSQL platforms.
• Oversee the full lifecycle of data ingestion, transformation, and framework development.
• Implement source control and CI/CD pipelines to ensure high-quality releases.
• Collaborate with BI and analytics teams, supporting their fast-paced data needs.
• Lead technical projects, provide mentorship to junior engineers, and ensure best practices.
• Maintain high standards of data quality, resilience, and governance through comprehensive testing.

Only candidates with an existing legal right to work in the European Union will be considered for this role.