Kenvueは現在、以下求人を募集しております。
Lead Analyst - CVD Platform Data & Automation私たちがしていること
私たちKenvueは、日々のケアが持つ驚くべき力を信じています。100年以上の伝統と科学に根ざし、Neutrogena®, Aveeno®, Tylenol®, Listerine®, Johnson’s® and BAND-AID®など、皆様が既にご存じでご愛用いただいているアイコニックなブランドを提供しています。科学は私たちの情熱であり、ケアは私たちの才能です。
Who We Are
私たちのグローバルチームは、インサイトとイノベーションに情熱を注ぎ、最高の製品をお客様にお届けすることに全力を注ぐ、多様で優秀な22,000人以上の社員で構成されています。専門知識と共感力を備えたKenvuerであることは、毎日何百万人もの人々の生活に影響を与える力を持つことを意味します。私たちは、人を第一に考え、全身全霊をもってケアし、サイエンスで信頼を獲得し、勇気をもって解決します。私たちとあなた自身の未来を、共に切り開いていきましょう。
Role reports to:
SR MANAGER SL OMNI CHANNEL場所:
Asia Pacific, India, Karnataka, Bangalore勤務地:
ハイブリッドあなたがすること
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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