As a Data Engineer, you will design, build, and maintain modular data pipelines that support efficient refresh cycles, integrate AI/ML models, and ensure reliable, scalable, and secure data environments. You will collaborate with cross-functional teams to deliver high-quality data solutions that meet business and operational needs.
What You'll Do and How You'll Succeed
- Design, develop, and maintain pipelines using Airflow.
- Build modular and incremental data flows to optimise refresh cycles and minimise redundant runs.
- Develop ETL/ELT workflows to ingest and transform data from telecom and operational systems.
- Integrate APIs and external data sources into pipelines.
AI/ML Integration & Governance
- Collaborate with cross-functional teams to integrate AI/ML models into production environments.
- Ensure data quality, version control, and governance through documentation and reusable components.
- Contribute to domain logic validation with business and analytics teams.
- Participate in troubleshooting and optimisation of data processes.
- Ensure reliability, scalability, and performance of data workflows.
- Implement best practices for access management and governance.
We'd Love to Hear From You If...
Experience
- You have 3–7 years of hands‑on experience in data engineering or related roles.
- You have experience designing incremental and modular pipelines.
- You have experience integrating APIs and external data sources.
Technical Expertise
- You have strong proficiency in Python, SQL, and modern frameworks (Airflow, Spark).
- You have working knowledge of Azure data ecosystem (Data Factory, AzureML, Synapse, Blob Storage, Azure SQL).
- You have familiarity with AWS services (S3, ECS, RDS, EC2).
- You have strong understanding of data governance, versioning, and access management.
Ways of Working
- You solve problems with structured thinking and clear communication.
- You collaborate effectively with business, analytics, and engineering teams.
- You balance technical depth with delivery timelines and operational reliability.