Tech Stack
AWSAzureCloudETLGoogle Cloud PlatformKafkaPythonSparkSQL
About the role
- You will play a key role in enabling data accessibility, quality, and scalability across diverse industries and mission-critical systems.
- Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
- Develop and optimize data architectures using cloud and on-premise technologies.
- Implement best practices for data quality, security, and compliance.
- Work closely with data scientists, software engineers, and business stakeholders to ensure data availability and usability.
- Monitor and improve data system performance and reliability.
- Architect and implement robust data pipelines and infrastructure that support high-performance analytics and AI solutions.
Requirements
- Data Pipeline Development : Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
- Infrastructure & Architecture : Develop and optimize data architectures using cloud and on-premise technologies.
- Data Governance : Implement best practices for data quality, security, and compliance.
- Collaboration : Work closely with data scientists, software engineers, and business stakeholders to ensure data availability and usability.
- Performance Optimization : Monitor and improve data system performance and reliability.
- Education : Bachelor’s or Master’s in Computer Science, Engineering, or related field.
- Experience : 6+ years in data engineering, with experience in large-scale data systems.
- Technical Skills : Expertise in Python, SQL, Spark, Kafka, and cloud platforms (AWS, Azure, GCP).
- Architecture Knowledge : Familiarity with data lake, data warehouse, and real-time streaming architectures.
- Language Skills : Fluent in English.