Job description
Job Purpose
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.
Key Result Responsibilities
- 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
Key Result Responsibilities-Continued
- 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
Qualifications (Academic, training, languages)
- 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.
Work Experience
- 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.
This job post has been translated by AI and may contain minor differences or errors.
