Data Engineer
Responsibilities:
- Design and operate production-grade ETL/ELT data pipelines for data ingestion, transformation and loading.
- Integrate data from APIs, databases, enterprise systems, SaaS, files, streams and cloud services.
- Develop batch, incremental, CDC and event-driven data pipelines.
- Design data models, schemas, data lakes and analytical datasets.
- Implement data quality, validation, monitoring, reconciliation and reliability controls.
- Build trusted datasets for analytics, reporting, operational visibility and AI/ML use cases.
- Design secure data integration across on-premise, AWS, Azure and hybrid environments.
- Ensure appropriate data security, access control, governance, lineage and auditability.
- Monitor production pipelines, troubleshoot incidents and improve performance, reliability and cost.
- Apply software engineering practices including Git, automated testing, CI/CD and Infrastructure as Code.
Requirments:
Minimum 3–5 years of experience in data engineering, cloud data engineering, analytics engineering, software engineering, or a related discipline
At least 2 years of hands-on experience designing, building, and operating production-grade data pipelines
Demonstrated experience with data extraction, ingestion, ETL/ELT, transformation, data modelling, and data quality
Experience using AWS and/or Azure native data capabilities
Experience integrating data from APIs, databases, enterprise systems, files, or streaming sources
Experience implementing batch, incremental, CDC, and/or event-driven data pipelines
Experience working with on-premises and/or cloud environments, with an understanding of hybrid integration patterns
Experience applying software-engineering practices such as version control, automated testing, CI/CD, monitoring, and Infrastructure as Code to data solutions