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Data Analytics Platform Engineer

Closing: Apr 29, 2024

5 days remaining

Published: Apr 23, 2024 (1 day ago)

Job Requirements

Education:

Work experience:

Language skills:

Job Summary

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QUALIFICATIONS
  • BS or MS in computer science or equivalent practical experience
  • At least 2-3 years of coding experience in a non-university setting.
  • Proficient understanding of distributed computing principles
  • Experience in collecting, storing, processing and analyzing large volumes of data.
  • Proficiency in understanding database technologies
  • Excellent written and verbal communication skills
  • Understanding of big data technologies: Cloudera/Hortonworks


Responsibilities
QUALIFICATIONS
  • BS or MS in computer science or equivalent practical experience
  • At least 2-3 years of coding experience in a non-university setting.
  • Proficient understanding of distributed computing principles
  • Experience in collecting, storing, processing and analyzing large volumes of data.
  • Proficiency in understanding database technologies
  • Excellent written and verbal communication skills
  • Understanding of big data technologies: Cloudera/Hortonworks


  1. Platform Architecture: Design and develop a scalable and extensible data analytics platform to support the organization's data-driven initiatives. Architect data pipelines, storage solutions, and analytics frameworks to handle large volumes of data efficiently.
  2. Data visualization - Creating visualizations such as charts, graphs, and dashboards to communicate insights effectively to stakeholders.
  3. Data Integration and Processing: Implement data ingestion pipelines to integrate data from various sources, including databases, data warehouses, APIs, and streaming platforms. Develop ETL (Extract, Transform, Load) processes to preprocess and clean raw data for analysis. 
  4. Analytics Tools and Technologies: Evaluate, select, and integrate analytics tools and technologies to support data exploration, visualization, and modeling. Implement and optimize databases, data warehouses, and analytics frameworks such as SQL, Hadoop, Spark, and Elasticsearch. 
  5. Scalability and Performance: Optimize data processing pipelines and analytics workflows for scalability, performance, and efficiency. Implement parallel processing, distributed computing, and caching mechanisms to handle large-scale data analytics workloads. 
  6. Data Governance and Security: Ensure compliance with data governance policies, regulatory requirements, and security best practices. Implement access controls, encryption, and auditing mechanisms to protect sensitive data and ensure data privacy and confidentiality. 
  7. Monitoring and Maintenance: Develop monitoring and alerting systems to track platform performance, data quality, and system health. Proactively identify and resolve issues to minimize downtime and ensure uninterrupted data analytics operations. 
  8. Automation and DevOps: Implement automation pipelines for infrastructure provisioning, configuration management, and deployment. Establish continuous integration and continuous deployment (CI/CD) processes to streamline platform development and operations. 
  9. Documentation and Training: Document platform architecture, data pipelines, and analytics workflows. Provide training and support to data analysts and data scientists to ensure effective use of the data analytics platform. 


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