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在 Kenvue，我們意識到日常護理的非凡力量。我們以一個多世紀的傳統為基礎，植根於科學，是標誌性品牌的品牌 - 包括您已經熟悉和喜愛的 NEUTRGENA®、AVEENO、TYLENOL®®、LISTERINE®、JOHNSON'S® 和 BAND-AID®。科學是我們的熱情所在;關心就是我們的才能。
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我們的全球團隊由 ~ 22,000 名才華橫溢的員工組成，他們的職場文化中，每個聲音都很重要，每一個貢獻都受到讚賞。 我們熱衷於洞察， 創新並致力於為我們的客戶提供最好的產品。憑藉專業知識和同理心，成為 Kenvuer 意味著每天有能力影響數百萬人。我們以人為本，熱切關懷，以科學贏得信任，以勇氣解決——有絕佳的機會等著您！加入我們，塑造我們和您的未來。有關更多資訊，請按兩下 here.
Role reports to：
SR MANAGER SL OMNI CHANNEL位置：
Asia Pacific, India, Karnataka, Bangalore工作地點：
混合你會做什麼
The Lead Analyst is responsible for delivering high-impact analytics, data, and reporting solutions that enable better decision-making and measurable business outcomes. This role combines strong data analysis, data engineering, and business partnership capabilities, leading through expertise, influence, empathy, and collaboration. The Lead Analyst owns work end to end—from understanding business needs through delivery, adoption, and value realization—while setting high standards for analytical rigor, scalability, and reliability across CVD solutions.
In addition to core analytics responsibilities, this role places emphasis on building, scaling, and stabilizing data platforms, pipelines, and automation capabilities that enable efficient, repeatable, and reliable analytics delivery across customers and teams.
Core Responsibilities & Expectations
Deliver end-to-end analytics solutions spanning data ingestion, transformation, modeling, reporting, and insight generation
Design, build, and support scalable data pipelines that ensure timely, reliable, and high-quality data availability
Apply strong data modeling practices to support consistent metrics, reusable datasets, and downstream analytics and reporting
Partner closely with business stakeholders to translate requirements into durable, scalable analytics solutions
Ensure analytics outputs are production-ready, well-documented, and aligned to defined business use cases
Identify opportunities to automate manual processes, reduce operational overhead, and improve speed to delivery
Lead through influence by setting technical standards, sharing informed opinions, and constructively challenging designs and approaches
Areas of Emphasis
Build and maintain robust data ingestion, transformation, and validation workflows across multiple data sources
Enable analytics, reporting, forecasting, and self-service use cases through reliable and well-structured data foundations
Improve platform performance, scalability, and cost efficiency while maintaining data quality and governance standards
Support transitions and migrations (e.g., data platform or tooling changes) with minimal business disruption
Standardize patterns, processes, and reusable components to improve consistency and delivery velocity across teams
Must Have
Strong hands-on experience with data analysis, transformation, and end-to-end analytics delivery
Experience designing and supporting scalable data pipelines in a modern cloud-based analytics environments (Databricks, Snowflake, Azure, SQL etc.)
Solid data modeling skills supporting consistent metrics, hierarchies, and reusable analytical structures
Proficiency in Python-based data engineering and analytics workflows
Experience delivering automation that reduces manual effort and improves reliability and timeliness
Strong collaboration and business partnership skills
Ownership mindset with accountability for platform reliability, efficiency gains, and downstream business enablement
Good to Have
Experience with distributed data processing and transformation approaches (e.g., PySpark-class workloads)
Exposure to analytics enablement for forecasting, planning, and performance management use cases
Experience supporting multi-team or contractor-based delivery models
Familiarity with data governance, access management, and data quality monitoring
Prior CPG, retail, or customer analytics platform experience
Ways of Working & Leadership Expectations
Leads through expertise, influence, and collaboration rather than formal authority
Works closely with business and delivery partners to ensure data foundations enable real outcomes
Demonstrates strong self-awareness, accountability, and constructive challenge
Continuously improves platforms, processes, and patterns to increase effectiveness and scalability
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