AI-Driven Data Infrastructure Specialist

7 hours ago

Singapore beBeeDataInfrastructure Full time

AI-Driven Data Infrastructure Specialist

The AI-Driven Data Infrastructure Specialist plays a pivotal role in building and maintaining data infrastructure to detect unauthorized privileged access in logs with confidence scoring using AI and data engineering best practices.

Key Responsibilities:

  • Core Responsibilities:
    • Design and implement ETL/ELT processes for large-scale data ingestion and transformation
    • Manage and maintain scalable data warehousing solutions that cater to business intelligence needs
    • Deploy machine learning models into production environments to drive informed decision-making
    • Ensure data quality, consistency, and integrity across various data sources through rigorous testing and validation
    • Collaborate with cross-functional teams and software engineers to develop data-driven features and products
  • Senior Specialists:
    • Define and implement best practices for data and DevOps processes to ensure agility and efficiency
    • Provide technical leadership and guidance across teams to promote knowledge sharing and skill development
    • Mentor junior specialists and contribute to capability building through training and development programs

Must-have Skills:

  • Strong proficiency in Python programming language for automation and scripting
  • Experience with SQL/NoSQL/Graph databases for efficient data storage and retrieval
  • Familiarity with cloud platforms (GCP, AWS) and related technologies (SPLUNK)
  • Experience with CI/CD setups for automated testing and deployment
  • Solid foundation in algorithms, database structures, and integration strategies for optimal data management
  • Proven ability to design scalable data solutions that meet business requirements
  • Nice-to-have / Preferred Skills:
    • Familiarity with building scalable APIs and deploying ML/AI solutions for predictive analytics
    • Understanding of data-intensive frameworks and event-driven architectures (e.g., Kafka, SQS)
    • Exposure to graph data structures for complex data analysis and visualization

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