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Staff Engineer, Big Data
Nagarro
Gurugram, IND
Full-time
Senior
Onsite
Discovered 6 days ago
Google BigQueryPythonSQLPySparkKubernetesApache Airflow
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Google BigQueryPythonSQL
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Google BigQueryPythonSQLPySparkKubernetesApache Airflow
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REQUIREMENTS
- Total experience: 5.5+ years. • Strong experience in Data Engineering, Cloud Engineering, or Big Data Engineering. • Must-have expertise in Google BigQuery, Python, SQL, PySpark, GCP fundamentals, and Kubernetes. • Strong hands-on experience with BigQuery and SQL, including writing and optimizing complex queries for large-scale data processing. • Strong programming experience in Python, with hands-on experience in developing scalable applications and APIs. • Strong experience with PySpark/Spark and familiarity with Big Data technologies such as Hive. • Hands-on experience with Apache Airflow or similar orchestration services for building and managing data workflows. • Experience with GCP serverless services, particularly Cloud Functions and Cloud Run. • Good experience with Docker and Kubernetes for containerization, deployment, orchestration, scaling, and troubleshooting. • Experience with Python API frameworks such as FastAPI, Flask, or Django; FastAPI is preferred. • Experience with CI/CD pipelines, preferably using GitLab CI/CD and Octopus Deploy, along with Git-based version control. • Good understanding of Terraform and Infrastructure as Code (IaC) for provisioning and managing cloud resources. • Good understanding of GCP networking, VPC, load balancing, IAM, API security, monitoring, logging, and alerting. • Strong troubleshooting, analytical, problem-solving, communication, and collaboration skills.
RESPONSIBILITIES
- Design, implement, and maintain scalable and reliable Big Data and cloud solutions using GCP, BigQuery, PySpark, Python, and Airflow. • Develop and maintain data workflows and pipelines using Apache Airflow and other orchestration services. • Design, develop, and optimize BigQuery and SQL queries for high-volume data processing and analytics workloads. • Develop and deploy cloud-native and serverless applications using GCP Cloud Functions and Cloud Run. • Develop scalable APIs using Python frameworks such as FastAPI, Flask, or Django. • Containerize applications using Docker and deploy, manage, and troubleshoot workloads on Kubernetes. • Implement and maintain CI/CD pipelines for automated build, testing, and deployment using GitLab CI/CD, Octopus Deploy, or similar tools. • Implement Infrastructure as Code (IaC) using Terraform to provision and manage GCP cloud resources. • Configure and manage IAM policies, VPC networking, load balancing, API access controls, and security mechanisms. • Implement monitoring, logging, alerting, and observability solutions to proactively identify and resolve system issues. • Write comprehensive unit tests and implement quality practices to ensure application reliability and maintainability. • Troubleshoot production issues, perform root cause analysis, and implement preventive measures to improve system stability. • Collaborate with Data Engineering, Cloud, DevOps, Infrastructure, Security, and Application teams to deliver scalable and reliable solutions. • Continuously improve data pipelines, cloud architecture, automation, application performance, security, and operational efficiency through industry best practices.
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