Senior Data Engineer – Enterprise Data Platforms

We are seeking an experienced Senior Data Engineer to design, build, and operate enterprise‑scale data pipelines and datasets that support advanced analytics, enterprise reporting, and machine learning use cases. You will play a key role in shaping the Enterprise Data Strategy and Common Data Model while working closely with data scientists, analysts, and business stakeholders in an Agile environment.

• Design, build, and maintain datasets and data pipelines for analytics and data science use cases
• Prepare, troubleshoot, and optimize data pipelines to ensure reliability, performance, and data quality
• Co‑design and evolve the Enterprise Data Strategy and Common Data Model
• Implement and operate core Data Platform processes and services
• Develop and maintain data pipelines tailored for Data Scientists and analytics teams
• Maintain and govern model JSON schemas
  , metadata, and data definitions
• Identify, analyse, and resolve data quality and data consistency issues
• Support:
  • Enterprise reporting and analytics
  • Machine Learning operations (MLOps)
• Collaborate with stakeholders across IT, data science, analytics, and business domains using Agile delivery methods

• Proven experience working with Big Data platforms and technologies
  , including:
  • S3, Hive, Spark, Trino, MinIO
  • Kubernetes (K8S) and Kafka
• Strong experience with SQL-based and relational data systems
  , including PL/SQL
• Experience handling banking or financial data
  , including governance and regulatory considerations
• Hands‑on experience with large‑scale on‑premises Data Lake migrations
• Integration of Data Science workbenches such as:
  • KNIME, Cloudera, Dataiku (or similar platforms)
• Experience working in Agile environments (Scrum, SAFe)
• Proven stakeholder management and cross‑team collaboration skills

• Strong understanding of enterprise data reference architectures
• Expert‑level SQL and data modelling skills
• Python for:
  • Automation
  • Data processing
  • Notebooks and analytics workflows
• Data preparation techniques for:
  • Reporting
  • Advanced analytics
  • Machine learning
• Experience implementing and working with data quality frameworks
• Familiarity with streaming and event‑driven architectures using Kafka