Big Data Infrastructure Architect
4 days ago
SID Global Solutions APAC is looking for a highly skilled Big Data Infrastructure Architect to join our team. The ideal candidate will have 3-5 years of experience in designing and implementing scalable data architectures that enable accurate and efficient data flow across various systems.
This role will be focused on building and maintaining big data infrastructure, optimizing data pipelines, and collaborating with cross-functional teams to support data-driven decision-making.
Key Responsibilities:
- Design and Develop Data Pipelines: Build, maintain, and optimize scalable data pipelines to ingest, transform, and load data across various platforms (cloud and on-premise).
- ETL Process Management: Lead the development and optimization of ETL (Extract, Transform, Load) processes to ensure seamless data flow, integrity, and consistency across internal and external systems.
- Database Management: Manage and optimize relational and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra) for performance, reliability, and scalability.
- Cloud Data Infrastructure: Work with cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure to deploy and scale data solutions.
- Data Warehousing: Build and maintain data warehousing solutions, ensuring the efficient and reliable storage of large datasets.
- Data Quality Assurance: Ensure the accuracy, consistency, and availability of data by implementing data quality and validation checks across data pipelines.
- Automation and Monitoring: Automate routine tasks and implement monitoring to ensure data systems and pipelines run efficiently with minimal downtime.
Required Skills and Qualifications:
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
- 3-5 years of experience in data engineering or a similar role, ideally in a fast-paced, data-driven environment.
- Proficiency in SQL and experience working with relational databases.
- Strong programming skills in Python, Java, or Scala for building data pipelines.
- Experience with ETL tools for automating data workflows.
- Hands-on experience with cloud data services such as AWS (Redshift, S3, Lambda), Google Cloud Platform (BigQuery, Cloud Storage), or Microsoft Azure.
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