Data Platform Engineer

Bangalore/ Gurgaon / Mumbai
Payment – Payments India - Engineering /
Full-time /
Hybrid

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.

Must Have

    • BE/B-Tech/BS/Meng/MSc in computer science or a related technical field from top-tier colleges or equivalent practical experience
    • 2-5 years of experience working on platform infrastructure based on Cloud and open-source systems and automation.
    • Must be very good with at least 1 programming lang), preferred: 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