Data Engineer II

Design and deliver robust, scalable data pipelines and infrastructure components that ensure reliable, high-quality data availability for analytics, data science, and AI workloads. The role operates with growing technical ownership, taking end-to-end responsibility for assigned platform domains and contributing meaningfully to architectural decisions and engineering standards.

• Design, develop, and maintain scalable ETL/ELT pipelines that ingest, transform, and serve data from structured and unstructured sources across cloud environments
• Own assigned pipeline domains end-to-end — from requirements and design through to deployment, monitoring, and iterative improvement
• Implement and evolve data models that support analytics, BI, and machine learning consumption requirements
• Build and maintain orchestration workflows using tools such as Apache Airflow, dbt, or Azure Data Factory
• Optimize pipeline and query performance across cloud data platforms including Snowflake and Azure Synapse
• Define and implement data quality rules, automated testing, and alerting to ensure reliability and consistency of data outputs

• Contribute to architectural discussions and platform decisions, providing well-reasoned technical input and trade-off analysis
• Collaborate with Data Scientists, Analytics Engineers, and business stakeholders to translate data requirements into maintainable engineering solutions
• Conduct code reviews and support the development of Associate and DE I engineers through practical guidance
• Maintain clear documentation for all assigned pipelines, data models, and infrastructure components

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field. 
• Fluent in English Language.
• ITIL Certification is an advantage but not mandatory.
• Strong proficiency in SQL and Python for data transformation, automation, and pipeline development.
• Working knowledge of data orchestration and transformation tools (e.g., Apache Airflow, dbt).
• Understanding of data modelling techniques — dimensional modelling, star/snowflake schema, and medallion/lakehouse architecture patterns.
• Familiarity with batch and streaming data processing concepts (e.g., Spark, Kafka, Azure Event Hubs).
• Good grasp of software engineering fundamentals: version control (Git), CI/CD practices, and automated testing.
• Ability to communicate technical designs clearly and provide constructive feedback to junior team members.

• With 4–6 years of hands-on experience in data engineering or a closely related software engineering role
• Demonstrable experience delivering production-grade data pipelines in a cloud environment.
• Solid hands-on experience with cloud data platforms on Azure, AWS, or GCP (e.g., Azure Data Factory, Synapse Analytics, S3, Redshift, BigQuery).
• Experience with data warehousing platforms such as Snowflake or Azure Synapse, including query optimisation and access control.