Data Analyst

• Design, build, and maintain scalable data pipelines to collect, clean, transform, and validate structured & unstructured data from multiple internal and external sources.
• Collaborate with data engineers, cybersecurity teams, AI/ML & LLM/Agent teams, and business stakeholders to define data needs and ensure data accuracy, security, and reliability.
• Analyze cybersecurity datasets including SIEM logs, CVEs, vulnerabilities, and asset inventories to identify trends, anomalies, and actionable insights that strengthen security analytics.
• Develop and maintain data models, semantic layers, ontology relationships, lineage documentation, and metadata standards to support governance, interoperability, and transparency.
• Automate data workflows and scheduling using Airflow, Prefect, or Temporal; support CI/CD processes for data including validation, testing, and version control.
• Integrate and manage datasets across OCI and hybrid environments, ensuring secure connectivity via VPNs, private endpoints, and cloud storage services.
• Build and maintain dashboards and BI insights using Power BI, Looker, or Grafana to enable data-driven decisions for leadership and technical teams.
• Support GenAI/LLM initiatives by preparing datasets, curating data pipelines, performing feature engineering, and enriching metadata to enable high-quality model training.
• Contribute to AI-driven analytics (AI for BI) by integrating LLMs and Agent-based workflows with BI tools (Power BI, Looker, Tableau) to automate reporting and deliver deeper insights.
• Continuously improve data quality, documentation, and analytical processes through performance optimization, stakeholder feedback, and best-practice implementation.

Do you have experience in Version control?, * 5+ years of experience in SQL & Python (Pandas / PySpark) for data querying, analysis, transformation, and automation.

• Should have experience working with an AI team and should know how the data are prepared for GenAI solution.
• Should be able to perform end-to-end feature engineering, including extraction, selection, transformation, and enrichment of raw data to make datasets suitable for training ML/GenAI models.
• Strong hands-on experience with ETL & orchestration tools such as Airflow, Prefect, or Temporal to build robust data workflows.
• Expertise in data modeling, cleaning, transformation, and handling structured/unstructured formats, including JSON schema understanding.
• Good understanding of cybersecurity datasets such as SIEM logs, CVEs, vulnerability & asset inventory data.
• Experience with OCI or similar cloud platforms, including Object Storage, Autonomous DB, Logging, hybrid integrations via VPN & private endpoints.
• Knowledge of semantic modeling & ontology-based data relationships, metadata management, and lineage tracking.
• Experience working in AI-enabled analytics environments (e.g., AI for BI), integrating GenAI/LLM-based Agents with BI tools such as Power BI, Looker, or Tableau to automate insight generation, reporting workflows, and data-driven decision support.
• Hands-on with visualization tools (Power BI / Looker / Grafana), delivering performance dashboards and insights.
• Experience working on at least one production-grade GenAI/LLM solution (e.g., RAG, AI Agents, automated workflows) and collaboration with AI teams for data readiness.
• Familiarity with CI/CD for data pipelines, including testing, validation, and version control tools (e.g., Git, DVC), and experience with Agentic workflows.

$70,363.72 - $84,739.10 a year - Full-time