Data Product Manager – Analytics (m/f/d)

Founded in 1921, OLDENDORFF CARRIERS combines its history as a German shipowner with the network of one of the world's leading drybulk operators. We currently control some 750 chartered and owned vessels of 67 mio tdw, and we carry around 330 mio tons of raw materials and semi-finished products across the seven seas each year. Our customers can expect 100% performance. All the way.

In recent years, Oldendorff has modernised its IT landscape and established a dedicated Data & Analytics organisation to support better decision-making across Chartering, Finance, Fleet, and Operations. As part of our growth, we are expanding the Analytics team to 18 people.

Our focus is not on traditional reporting. We build analytical data products that deliver actionable insights, enable better decisions, and create measurable business value. Looking ahead, we are investing in AI-ready data products, conversational interfaces, and modern self-service experiences that transform how people interact with data.

Join Oldendorff Carriers and help make data-driven decision-making a real competitive advantage while supporting our long-term ambitions for sustainable growth and carbon neutrality by 2050.

Data Product Manager – Analytics (m/f/d)
Hamburg, Lübeck

• Act as the primary analytical partner for stakeholders in Chartering, Finance, Fleet, or Operations, translating business needs into clearly scoped data solutions.
• Own the roadmap and prioritization of your portfolio of data products in alignment with business priorities.
• Define and maintain the semantic layer for your data products, while ensuring business definitions, metrics, and AI-ready context.
• Enable self-service access to insights through AI-driven tools, conversational interfaces, and natural-language querying.
• Build and validate prototypes using modern AI tools and collaborate with Data Engineering to industrialise successful solutions.
• Use working proficiency in SQL and Python to explore data.
• Coordinate testing, rollout, adoption, and continuous improvement of your data products.

What success looks like

• Stakeholders involve you early because your insights improve decision-making.
• Your data products are trusted and actively used in day-to-day business decisions.
• Data Engineering receives clear, actionable requirements and can deliver efficiently.

Must-have

• At least 3 years of experience in an analytical, product, or stakeholder-facing role.
• Strong ability to translate business needs into actionable solutions.
• Excellent communication skills with both business and technical audiences.
• Working proficiency in SQL and Python.
• Experience working with KPIs, metrics, and business definitions.
• Genuine interest in applying AI tools within analytics and data products.
• A proactive, hands-on mindset and the ability to work effectively in ambiguous environments.
• Excellent written and spoken English.

Nice-to-have

• Experience in shipping, commodity markets, finance, or macroeconomics.
• Familiarity with Databricks

• A collaborative, international working environment with flat hierarchies.
• Fast decision-making and a strong culture of ownership.
• The opportunity to shape how data and AI are used across a global business.
• Plenty of room for initiative, new ideas, and personal growth.