Yotta Infrastructure
Data Platform Engineer
- Compensation
- Not disclosed
- Location
- Delhi / NCR
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Basic understanding of Apache Spark, Hive, Hadoop HDFS, and Airflow, including DAGs, scheduling, and task dependencies.
- Hands-on Python scripting, Linux command-line, system troubleshooting, container environments, Git, and basic ETL/distributed computing concepts.
- AWS L1 + Engineering skills with basic knowledge of core AWS services, monitoring, troubleshooting, IAM, compute, storage, networking, and cloud operations.
- Ability to support deployment, operations, monitoring, and troubleshooting of data platforms and AWS environments while working closely with senior engineers.
- Bachelors or any relevant Degree.
Nice to have
- Exposure to Jupyter / JupyterHub for collaborative notebook environments.
- Knowledge of Docker or Kubernetes.
- Knowledge of Hadoop and Apache Spark Cluster.
- Familiarity with SQL and working with structured/unstructured data.
- Experience with cloud platforms (AWS, GCP, or Azure) is a plus.
- Interest in big data (Hadoop), DevOps, and data pipeline automation.
Full description
Job Scope
We are looking for a Junior Data Platform Engineer to support and manage Apache Spark, Apache Airflow, JupyterHub, Python, and Linux-based data platforms.
The role includes platform monitoring, deployment, troubleshooting, performance optimization, and L2 operational support.
You will also provide AWS L2 support covering core AWS services, compute, storage, networking, IAM, monitoring, and incident troubleshooting.
The engineer will work closely with senior teams to ensure reliable, secure, and efficient operation of the data and cloud environment.
Total /Relevant Experience:
- 1+ years experience
Key Responsibilities:
- Assist in the setup, monitoring, and maintenance of Apache Spark clusters, Apache Hive, Hadoop and Airflow environments.
- Support the development and scheduling of data pipelines using Airflow DAGs and Python scripts.
- Help manage and configure JupyterHub for multi-user access and integration with Spark.
- Monitor cluster health and performance under guidance and assist in troubleshooting Spark job failures.
- Write and maintain Python automation scripts for data workflows, ETL, and process automation.
- Participate in code reviews, documentation, and deployment activities.
- Learn and follow best practices for distributed data processing, CI/CD, and DevOps workflows.
- Collaborate with senior engineers and data scientists to implement improvements and new features.
Must-have skill:
- Basic understanding of Apache Spark, Hive, Hadoop HDFS, and Airflow, including DAGs, scheduling, and task dependencies.
- Hands-on Python scripting, Linux command-line, system troubleshooting, container environments, Git, and basic ETL/distributed computing concepts.
- AWS L1 + Engineering skills with basic knowledge of core AWS services, monitoring, troubleshooting, IAM, compute, storage, networking, and cloud operations.
- Ability to support deployment, operations, monitoring, and troubleshooting of data platforms and AWS environments while working closely with senior engineers.
Good-to-Have Skills:
- Exposure to Jupyter / JupyterHub for collaborative notebook environments.
- Knowledge of Docker or Kubernetes.
- Knowledge of Hadoop and Apache Spark Cluster.
- Familiarity with SQL and working with structured/unstructured data.
- Experience with cloud platforms (AWS, GCP, or Azure) is a plus.
- Interest in big data (Hadoop), DevOps, and data pipeline automation.
Qualifications Criteria:
- Bachelors or any relevant Degree.
Interested candidate can share their update resume on mentioned mail id:abhalekar@yotta.com