Senior Data Engineer at NTT DATA responsible for building scalable data pipelines using Python and SQL. Focused on enhanced data quality and modern cloud-native ecosystems in an agile environment.
Responsibilities
Build scalable data pipelines: Design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting
Develop SQL and BigQuery solutions: Write and optimize advanced SQL transformations and build performant, cost‑efficient BigQuery data models
Develop Python workflows: Implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high‑quality code
Design data models and ensure quality: Build robust data models and apply validation practices to maintain accuracy and reliability
Build cloud‑native data solutions: Use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS to build and operate modern data platforms
Optimize performance and reliability: Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance
Collaborate using strong engineering practices: Work with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards
Requirements
University degree in computer science or a comparable qualification
At least 5 years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data ecosystems
Strong expertise in SQL and hands‑on experience building performant datasets in BigQuery (or similar cloud data warehouses)
Proven experience with Python and PySpark for scalable data processing in distributed environments
Solid understanding of data modeling, ELT/ETL patterns, and data quality best practices
Experience with Google Cloud Platform, particularly BigQuery, Dataflow, Cloud Composer, GCS, or equivalent cloud data services
Hands‑on experience building scalable data pipelines (batch and near real‑time) in a cloud‑native environment
Proficiency with version control, CI/CD pipelines, and automated testing frameworks
Ability to troubleshoot and optimize performance across compute, storage, and processing layers
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