Lead Analyst - CVD Platform Data & Automation

Was wir tun

Bei Kenvue erkennen wir die außergewöhnliche Kraft der täglichen Pflege. Aufbauend auf über einem Jahrhundert Tradition und verwurzelt in der Wissenschaft, sind wir das Haus der ikonischen Marken - darunter NEUTROGENA®, AVEENO®, TYLENOL,® LISTERINE®, JOHNSON'S® und BAND-AID®, die Sie bereits kennen und lieben. Wissenschaft ist unsere Leidenschaft; Fürsorge ist unser Talent.

Wer wir sind

Unser globales Team besteht aus ~ 22.000 brillanten Mitarbeitern mit einer Arbeitsplatzkultur, in der jede Stimme zählt und jeder Beitrag geschätzt wird. Wir sind leidenschaftlich an Erkenntnissen interessiert, Innovation und dem Engagement, unseren Kunden die besten Produkte zu liefern. Mit Fachwissen und Einfühlungsvermögen bedeutet ein Kenvuer zu sein, die Macht zu haben, jeden Tag Millionen von Menschen zu beeinflussen. Wir stellen den Menschen an die erste Stelle, kümmern uns leidenschaftlich, gewinnen Vertrauen in der Wissenschaft und lösen Lösungen mit Mut – und haben brillante Möglichkeiten, die auf Sie warten! Gestalten Sie mit uns unsere Zukunft – und Ihre. Für weitere Informationen, klicken Sie auf hier.

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Was Sie tun werden

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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