Lead Data Analyst

Job Description

Role Overview

As Lead Business Data Analyst, you will drive data analysis, engineering, and advanced analytics initiatives. You will collaborate with Data Product Managers, Data Owners, and global cross-functional teams to deliver impactful business outcomes. This role balances hands-on analytics, AI innovation, stakeholder engagement, and continuous process improvement.

Key Responsibilities

• Lead complex data analysis, modeling, and visualization activities to extract actionable insights and support strategic decision-making.
• Collaborate with Data Product Managers and business stakeholders to gather requirements, define analytics and AI-powered solutions, and ensure alignment with product and project goals.
• Identify opportunities where AI-powered solutions can enhance data insights, automation, and optimize business processes.
• Translate business requirements into technical features, epics, and user stories for Agile delivery, supporting delivery team as Scrum Master where required.
• Work closely with engineering teams to operationalize automated data pipelines and data science models while balancing technical dependencies and timely delivery.
• Document and improve data pipelines, processes, and data flows, ensuring quality, scalability, and security.
• Develop reports and self-service dashboards—clearly communicate complex findings, including AI-generated insights, to technical and non-technical audiences.
• Champion data governance, privacy, and responsible AI usage across analytics initiatives.
• Identify opportunities for simplification, optimization, and automation within data and AI workflows.
• Promote knowledge sharing by disseminating best practices, tools, and technical expertise across teams.
• Stay ahead of data and AI trends; promote the use of data analytics, generative AI and machine learning where appropriate.

Competencies

• Proficient with Agile methodologies and tools (eg. Jira, Confluence) for effective delivery.
• Ability to write detailed product requirements, user stories, and acceptance criteria for analytics and AI features.
• Experienced in managing backlogs based on business value, technical dependencies, and the potential impact of AI-driven features.
• Hands-on expertise in data analysis, modelling and visualization tools (eg. SQL, Python, PowerBI, Tableau).
• Proactive problem-solving, critical-thinking, and process improvement skills rooted in engineering mindset.
• Effective communicator: skilled at translating complex technical and AI concepts for diverse stakeholders, verbally and in writing.
• Ownership mindset: demonstrate accountability and commitment to delivering outcomes.
• Continuous learner: adapt to new technologies and thrive in dynamic, fast-paced environments.