Data Analytics Engineer

Henkel • Cleaning & Household Products • Chemical Manufacturing • Python • 2 days ago

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In office (New Cairo)

Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Oct 4, 2026. Henkel scores A on the Alion truth index.

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Henkel

Henkel is a German multinational chemical and consumer goods company headquartered in Düsseldorf, organised into two business areas, Adhesive Technologies and Consumer Brands, with brands such as Loctite, Persil and Schwarzkopf. Founded in 1876 and listed in the DAX, it operates worldwide, with its North American headquarters in Rocky Hill, Connecticut, and large teams in Stamford, Boulogne-Billancourt, Shanghai, Brussels and Bratislava. Its vacancies include field sales and sector managers, production planners, quality engineers, engineering project managers, R&D and packaging interns, enterprise architects, logistics managers and legal counsel.

henkel.com • HQ: Düsseldorf, Germany • Hair Care Products • Cleaning & Household Products • FMCG & Consumer Brand Groups • Chemical Manufacturing • 1001-5000 employees • Est. 1876

Tech stack of Henkel

About this Position In this role, you will design and build scalable data assets and analytical solutions that enable data-driven decision-making across BI Team. What you´ll do Design, build, and maintain analytical data models and cubes (e.g., Azure Analysis Services / semantic models) to support reporting and advanced analytics Develop scalable data

pipelines on Databricks using Python and Spark to transform raw data into trusted data assets Build and manage end-to-end data assets, from ingestion to curated datasets ready for BI consumption Ensure data quality, consistency, and performance optimization across analytics solutions Collaborate closely with business stakeholders, analysts, and data

teams to translate requirements into scalable solutions Contribute to modern analytics architecture, best practices, and reusable frameworks Leverage AI-powered tools (e.g., Copilot, Genie, LLM-based solutions) to improve development efficiency and explore new use cases Continuously explore new technologies and proactively drive improvements within

the data landscape

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