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