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Data Architect at Infosys Singapore & Australia – Singapore | Jobyssey

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Data Architect

Data Architect

Infosys Singapore & Australia · Singapore, Singapore, Singapore · 3 years ago

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Category: Architecture
Experience: 12+ years

analytics aws azure big-data cloud data-engineering data-governance data-lake data-pipelines data-science data-visualization data-warehousing

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machine-learning

We are looking for a highly experienced and skilled Data Architect to join our team. The ideal candidate will have 12-15 years of experience in architecting solutions of data engineering, focusing on ELT and PySpark/Hadoop workloads. In addition to strong solution and delivery skills, the ideal candidate will also have a view on business growth and managing stakeholders.

Responsibilities:

Design and implement high-performance, scalable, and secure data architectures.
Work with business stakeholders to understand their data needs and translate them into technical requirements.
Design and develop data pipelines and workflows using ELT principles and PySpark/Hadoop
Optimize data pipelines and workflows for performance and efficiency
Work with data scientists and engineers to ensure that data is accessible and usable for analytics and machine learning
Implement data governance and security best practices
Manage and mentor data engineers
Contribute to the overall data engineering strategy and roadmap

Requirements

Qualifications:

12-15 years of experience in data engineering, with a focus on ELT and PySpark/Hadoop workloads
Strong experience in designing and implementing high-performance, scalable, and secure data architectures.
Experience with data governance and security best practices
Experience in managing and mentoring data engineers
Excellent communication and interpersonal skills
Ability to work independently and as part of a team
Strong problem-solving and analytical skills

Desired Skills:

Experience with cloud computing platforms such as AWS, Azure, or GCP
Experience with big data technologies such as Spark, Hadoop, Hive, and Kafka
Experience with data warehousing and data lakes
Experience with DevOps and MLOps practices

Experience with data science and machine learning, streaming data processing
Experience with real-time analytics, data visualization and reporting tools