Staff Engineer, Big Data

**👋🏼**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 (18500+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in!

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.

Bachelor’s or master’s degree in computer science, Information Technology, or a related field