Senior Data Engineer
Summary
Senior backend/data engineer (8+ years) who designs, migrates, and operates scalable batch ETL pipelines and data transformations using Apache Spark, Java, and Python, while also building REST APIs and CI/CD-driven deployment for reliable production data processing.
Backend / Data Engineer Job Description
Role: Backend / Data Engineer Experience: 8+ Years Location: Noida, Mumbai, Pune, Bangalore, Kochi Employment Type: Full-time
Role Overview We are looking for a skilled Backend/Data Engineer to design, migrate, develop, and operate scalable batch data pipelines. The role involves end-to-end ownership of ETL workflows, data transformations, backend services, and data processing using modern engineering practices.
Key Responsibilities
- Migrate and modernize end-to-end batch data pipelines, including ETL workflows and data transformations.
- Design, develop, and maintain scalable data pipelines using Apache Spark, Java, and Python.
- Build and integrate REST APIs for data and backend services.
- Develop efficient data transformation and processing solutions for large datasets.
- Monitor, troubleshoot, and optimize production data pipelines for reliability and performance.
- Implement automated deployment and release processes using CI/CD pipelines.
- Collaborate with engineering, data, and business teams to understand requirements and deliver robust solutions.
- Follow best practices for code quality, testing, version control, documentation, and production support.
Required Skills
- Strong hands-on experience with Java and Python.
- Expertise in Apache Spark and distributed data processing.
- Experience building, migrating, and operating batch data pipelines and ETL workflows.
- Strong understanding of data transformation and integration.
- Experience developing REST APIs.
- Hands-on experience with CI/CD, Git, and DevOps practices.
- Strong debugging, troubleshooting, and performance-tuning skills.
Good to Have
- Experience with cloud data platforms such as AWS/Azure/GCP.
- Knowledge of data lakes, databases, and modern data engineering architectures.
- Experience with workflow orchestration tools such as Airflow or similar platforms.
- Exposure to containerization and Kubernetes/Docker