• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Qualifications

• Bachelor’s or master’s degree in computer science, Engineering, or related field.
• Solid understanding of data analytics principles, techniques, and methodologies.
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with data processing frameworks such as Apache Spark, Apache Hadoop, or Apache Flink.
• Familiarity with database systems such as SQL databases, NoSQL databases, and distributed file systems.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.
• Ability to work independently and manage multiple priorities in a fast-paced environment

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