Lead Data Analyst

Dyson; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Dyson seeks a Lead Business Data Analyst to drive data analysis, AI-enabled insights, and analytics initiatives. You will collaborate with global teams to deliver impactful business outcomes, balancing hands-on analytics with stakeholder engagement and continuous process improvement.

Lead complex data analysis, model building, and visualization while driving Agile delivery and governance. A strong emphasis on AI, data pipelines, and cross-functional collaboration defines this role.

Qualifications

• Proven experience in data analysis and visualization.
• Experience with AI-powered analytics warranted.
• Strong communication with stakeholders.
• Ability to translate requirements into tech specs.

Responsibilities

• Lead data analysis, modeling, and visualization to deliver actionable insights.
• Collaborate with Data Product Managers and stakeholders to gather requirements.
• Identify AI opportunities to enhance insights and automate processes.
• Translate requirements into features, epics, and user stories for Agile delivery.
• Support delivery team as Scrum Master when required.
• Operate with engineering to deploy automated data pipelines and models.
• Document data pipelines and data flows for quality, security, and scalability.
• Develop reports and dashboards; communicate findings to diverse audiences.
• Promote data governance, privacy, and responsible AI usage.
• Share best practices and technical expertise across teams.
• Stay ahead of data/AI trends; apply generative AI where appropriate.
• Proficient with Jira and Confluence for delivery.
• Manage backlogs based on value and dependencies.
• Hands-on data tools: SQL, Python, PowerBI, Tableau.

Skills

Data analysis

Data modeling

Visualization

SQL

Python

Power BI

Tableau

Jira

Confluence

AI concepts

Stakeholder mgmt

Agile

Tools

SQL databases

Python

Power BI

Tableau

Jira

Confluence

Job description

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.
• 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.