Big Data Scientist

2 days ago

Pune, Maharashtra, India beBeeData Full time ₹ 15,00,000 - ₹ 30,00,000
About the Role

As a Deputy Manager GCP Data Engineer, you will be responsible for developing applications in Google Cloud Big Data environments to analyze data sources and design, code, test, debug, automate, and maintain data pipelines.

Key Responsibilities
  • Automate Machine Learning Models and ML pipelines on Google Cloud
  • Optimize Cloud processes with respect to cost, runtime, and stability
  • Develop real-time streaming applications using Kafka, Pub/Sub, and similar technologies
Requirements
  • Professional Google Cloud Data Engineer
  • Analytical technical skills to predict consequences of configuration changes (impact analysis), identify root causes that are not obvious, and understand business requirements
  • Excellent communication with different stakeholders (business, technical, project)
  • Good understanding of overall Big Data and Data Science ecosystem
  • Relevant work experience (5-9 years)
  • Experience with building and deploying containers as services using Swarm/Kubernetes
  • Good understanding of container concepts like building lean and secure images
  • Understanding modern DevOps pipelines
  • Experience with stream data pipelines using Kafka or Pub/Sub (mandatory for Kafka resources)
  • Strong programming experience in OOP language like Python, Java, or Scala
  • Experience working in agile projects
Accountabilities
  • Design, build, and manage Big data ingestion and processing applications on Google Cloud using Big Query, Dataflow, Composer, Cloud Storage, Dataproc
  • Performance tuning and analysis of Spark, Apache Beam (Dataflow) or similar distributed computing tools and applications on Google Cloud
  • Good understanding of Google Cloud concepts, environments, and utilities to design cloud optimal solutions for Machine Learning Applications
  • Build systems to perform real-time data processing using Kafka, Pub-sub, Spark Streaming, or similar technologies
  • Manage the development life-cycle for agile software development projects
  • Convert a proof of concept into an industrialization for Machine Learning Models (MLOps)
  • Provide solutions to complex problems
  • Deliver customer-oriented solutions in a timely, collaborative manner
  • Proactive thinking, planning, and understanding of dependencies
  • Develop and implement robust solutions in test and production environments

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