Data Engineering Expert
1 month ago
Springer Nature Group is a leading publisher of scientific books, journals, and magazines with over 3000 journal titles. We are seeking an experienced Data Engineer to join our team in Pune.
About UsWe're looking for a skilled Data Engineer to work with us in evolving a cross-platform view of our submission data. This involves driving workflow management, customer experience, and business reporting in up to 30 teams.
Job DescriptionWe offer a competitive salary of $120,000 per year, making this a great opportunity for experienced professionals. As a Senior Data Engineer at Springer Nature Group, you will be responsible for designing, developing, and maintaining scalable data pipelines on Google Cloud Platform (GCP). You will work with BigQuery, Python, DBT, Terraform, and GIT to implement and optimize data storage solutions for large-scale processing.
You will collaborate with data scientists, analysts, and stakeholders to meet data requirements, ensuring data quality and integrity throughout the data lifecycle. Additionally, you will implement CI/CD pipelines for data applications and manage infrastructure using Terraform. Utilizing DBT for data modeling, testing, and documentation, you will use GIT for version control and code collaboration.
Responsibilities- Design and develop scalable data pipelines on GCP
- Implement and optimize data storage solutions with BigQuery
- Develop, test, and deploy data transformation workflows using Python
- Collaborate with data scientists, analysts, and stakeholders
- Ensure data quality and integrity
- Implement CI/CD pipelines for data applications
- Manage infrastructure using Terraform
- Utilize DBT for data modeling, testing, and documentation
- Use GIT for version control and code collaboration
We're looking for someone with over 5 years of experience in data/software engineering on a cloud platform (AWS/GCP/Azure) using tools such as DBT and programming languages like Python, Scala, or Java. You should have strong SQL and data problem-solving skills, along with experience with data modeling and transformation tools like DBT. A solid understanding of modern data engineering practices and the ability to factor in non-functional aspects of data pipeline development are also essential.
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