Data Engineer
Hyderabad, TG, IN
NTT DATA
NTT DATA helps clients transform through consulting, industry solutions, business process services, IT modernization and managed services.Req ID: 291171
NTT DATA Services strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a Data Engineer to join our team in Hyderabad, Telangana (IN-TG), India (IN).
- Design, implement and maintain Azure Synapse data pipelines and transformation (Spark SQL, pySpark, Python) for data ingestion, transformation and loading
- Able to analyze, recreate and improve business logic from AWS based data pipelines and transformations into Azure
- Create and implement source to target mappings for a variety of first party and third party data sources
- Design and maintain data modeling/master hierarchies as needed
- Utilize and improve GitHub CI/CD actions to deploy artifacts in multiple environments
About NTT DATA Services
NTT DATA is a $30+ billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long-term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are also one of the leading providers of digital and AI infrastructure in the world. NTT DATA is part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: AWS Azure CI/CD Consulting Data pipelines GitHub ML infrastructure Pipelines PySpark Python R R&D Spark SQL
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