GCP Data Engineer/Data Architect

www.nttdata.com/it

Responsibilities

A Data Architect is an IT expert that enables data-driven decision making by collecting, transforming, and publishing data. In NTT Data, a Data Architect should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance, scalability and efficiency, reliability and fidelity, flexibility and portability. The main mission of a Data Architect is to turn raw data into information creating insight and business value.

• Build large-scale batch and real-time data pipelines with data processing frameworks in GCP cloud platform.
• Use an analytical, data-driven approach to drive a deep understanding of fast-changing business needs.
• Collaborate with the team to evaluate business needs and priorities, liaise with key business partners, and address team needs related to data systems and management.
• Participate in project planning by identifying milestones, deliverables, and resource requirements; track activities and task execution.

Requirements

Required Skills

• Bachelor’s degree in Computer Science, Computer Engineering, or a relevant field.
• At least 5-10 years of experience in a data engineering role.
• Expertise in software engineering using Scala, Java, or Python.
• Advanced SQL skills, with a preference for using BigQuery.
• Good knowledge of Google Managed Services such as Cloud Storage, BigQuery, Dataflow, Dataproc, and Data Fusion.
• Experience using workflow management tools.
• Good understanding of GCP architecture for batch and streaming data.
• Strong knowledge of data technologies and data modeling.
• Expertise in building modern, cloud-native data pipelines and operations, following an ELT philosophy.
• Experience with Data Migration and Data Warehousing.
• Strong understanding of how to organize, normalize, and store complex data to enable both ETL processes and end-user access.
• Passion for designing ingestion and transformation processes for data from multiple sources to create cohesive data assets.
• Good understanding of developer tools, CI/CD, etc.
• Excellent communication skills; empathetic with end users and internal customers.

Nice-to-have:

• Experience with Big Data ecosystems such as Hadoop, Hive, HDFS, HBase.
• Experience with Agile methodologies and DevOps principles.