Data Engineer - (Django Must- Big Data Platform)

Role: Data Engineer – (Django Must- Big Data Platform)
Company: PayU
Location: Bangalore/Mumbai/Gurgaon
Job responsibilities:
Building, maintaining, and monitoring sustainable infrastructure with a focus on developing and maintaining high-volume, low-latency data platforms using latest opensource frameworks, highly available RESTful services, and back-end Cloud based/open source systems Execute day to day operation of the data platform to ensure top-notch performance of data platform systems for customer-facing applications Work on features to make the data Platform more robust and resilient Drive automation of application deployment for production environments Identify and support areas for process and efficiency improvement within platform operations Taking ownership of your projects, and have the work in small cross functional teams to drive projects to closure Highly collaborative with a strong belief in continuous improvement. Ability to stay focused and calm in a fast-paced environment. Requirements to be successful in this role:
BE/B-Tech/BS/Meng/MSc in computer science or a related technical field from top-tier colleges or equivalent practical experience 2.5-5 years of experience working on platform infrastructure based on Cloud and open-source systems and automation. Must be very good with programming lang : Python
Django/Flask (preferred, must be experienced in building APIs) Databases: SQL querying & db understanding (RDBMS: mysql, postgresql; Nosql: mongodb, elasticsearch, dynamodb)
Familiarity with big data tools & distributed systems (spark, redshift, kafka) Cloud Computing (Any of AWS/Azure/GCP), preferred: AWS
Orchestration: airflow, docker
Good to Have:
Monitoring/Logging frameworks (ELK, Grafana) Scalable Compute solutions (Kafka, Snowflake) Workflow management libraries and tools Container orchestration systems like Kubernetes Code-as-Infrastructure/Pipeline-as-Code (Terraform/Chef) Understanding and functional experience in Big Data Engineering practices: Data Warehousing, ingestion pipelines, ELT, data migration, QC