We're Nagarro.
We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (17500 experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!
REQUIREMENTS:
- Total experience: 4+ years.
- Excellent knowledge and experience in
GCP Bigdata Engineer
role. - Strong working experience with architecture and development in
Spark,
Python/Scala, GCP Services (Pubsub, Data Flow, Airflow, Dataproc etc.), GCP Lakehouse, SQL
. - Strong experience with Google Cloud Platform (GCP) services.
- Hands-on experience with: Cloud Storage (GCS), BigQuery, Dataproc, Cloud Composer (Airflow), Pub/Sub, Cloud Functions, Data Processing & Development.
- Strong knowledge of Apache Spark (PySpark/Scala Spark).
- Experience in building ETL/ELT pipelines.
- Strong SQL development and query optimization skills.
- Experience handling large-scale datasets and distributed processing frameworks.
- Lakehouse & Data Architecture
- Good understanding of GCP Lakehouse architecture and modern data platform concepts.
- Experience with data modeling, partitioning, and performance optimization.
- Understanding of data warehousing and analytics best practices.
- DevOps & Engineering Practices
- Experience with Git-based version control.
- Knowledge of CI/CD pipelines and deployment automation.
- Familiarity with Agile/Scrum methodologies.
RESPONSIBILITIES:
- Design, develop, and maintain scalable data pipelines using GCP services.
- Build and support Lakehouse-based data platforms for ingestion, transformation, and analytics workloads.
- Develop ETL/ELT processes for handling structured and semi-structured data.
- Implement batch and distributed data processing solutions using Dataproc and Spark.
- Create and manage workflow orchestration using Apache Airflow.
- Optimize data pipelines for performance, scalability, reliability, and cost efficiency.
- Work closely with data analysts, architects, and business stakeholders to understand requirements and deliver solutions.
- Ensure data quality, governance, security, and compliance standards are followed.
- Troubleshoot production issues and provide timely resolutions.
- Participate in code reviews, testing, deployment, and documentation activities
