Director DataOps _ Data Analytics and AI

Director DataOps Data, Analytics & AI

Lead the company's enterprise data, analytics, and AI initiatives. This is a hands-on technical leadership role responsible for defining data and analytics strategy, partnering with business leaders, and building scalable analytics and AI solutions that drive business decisions.

Key Responsibilities

Enterprise Data Leadership

• Lead the enterprise data strategy, including building a scalable cloud data platform and long-term analytics roadmap.
• Define enterprise data models, KPIs, and governance frameworks to ensure quality, security, and data management best practices.
• Manage role-based access control across all data sources
• Build and manage data ingestion pipelines from structured and unstructured data sources

AI & Analytics

• Develop AI-powered predictive models and dashboards to drive real-time decision-making and automate reporting.
• Partner with leadership to define KPIs and deliver actionable insights through statistical analysis and machine learning.

Technical Leadership

• Design and implement modern data architecture, including scalable pipelines that integrate disparate business systems.
• Serve as a technical contributor for SQL, Python, and code reviews, while evaluating and introducing emerging AI tools.

Business Partnership

• Serve as a trusted advisor to executive leadership, translating complex business challenges into actionable technical solutions.
• Collaborate cross-functionally with business units to influence organisational priorities using data-driven insights.

Team Leadership

• Build and mentor a high-performing, cross-functional data and analytics team.
• Establish engineering standards and analytics best practices to foster a culture of innovation and continuous improvement.

Qualifications

• 8+ years in data engineering, analytics, BI, or data science with technical leadership experience.
• Strong Python experience for data engineering and analytics.
• Familiarity with modern AI technologies including LLMs, vector databases, RAG, and AI agents.
• Experience building enterprise data platforms and AI-enabled analytics.
• Tech stack (GCP, BigQuery, Python, AI/LLMs, etc.)

For more details, please reach out to us at [email protected]