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 are currently operating around 800 owned and chartered vessels and transporting approximately 415 million tons of cargo annually. Our global operations focus on spot business, industrial contracts, and offshore transshipment, primarily carrying iron ore, coal, and agricultural products.
Our 4,500 employees from 60 countries include more than 1,000 office-based colleagues across 20 locations and over 3,000 seafarers.
Our customers can expect 100% performance. All the way.
About the Role
In recent years, Oldendorff has modernised its IT landscape to meet state-of-the-art standards. Building on this foundation, we established a dedicated Data & Analytics organisation to support better decision-making across our core business areas, including Chartering, Finance, Fleet, and Operations. This round of hiring will grow the Analytics team to 18 people.
Rather than producing traditional reports, we build analytical data products that provide clear, consistent, and actionable insights, enabling better decisions and creating real business value. Our ambition goes beyond dashboards: we are moving toward AI-ready data products, modern app-style experiences, and conversational, chat-based interfaces as a core part of how our organisation will interact with data.
We aim to use data to support sustainable growth, strengthen our market leadership, and contribute to our goal of carbon neutrality by 2050.
Join Oldendorff Carriers as a Data Product Manager in Analytics and help make data-driven decision-making a genuine strength of Oldendorff. You will focus on one of our core business areas, such as Chartering, Finance, Fleet or Operations, acting as the primary link between that business and Data Engineering. The business defines the problem and the outcome it needs; you own how that outcome is achieved, from initial scoping through to real adoption in daily decision-making.
Job Responsibilities
- Act as the main analytical partner for stakeholders in areas such as Chartering, Finance, Fleet and Operations, translating business problems and desired outcomes into clearly scoped requirements and solution design, working iteratively with Data Engineering on feasibility and challenging technical constraints where they affect what gets delivered
- As part of an Analytics team that plans and prioritises together, own the roadmap across your portfolio of narrowly scoped data products and shape how work is sequenced based on business priorities
- Own and maintain the semantic layer for your data products, ensuring consistent business definitions, metrics and AI-readable context, aligned with the organisation's master data and single-source-of-truth principles
- Enable self‑service access to your data product's insights, primarily through AI‑driven tools such as conversational interfaces, natural‑language querying and proactive AI‑generated insights, so business users can get answers themselves. Where a dedicated interface is genuinely needed, prototype it yourself using AI tools to move fast and validate the concept; any prototype intended for production is handed over to Data Engineering for industrialisation once it passes defined maturity and governance criteria, while you retain ownership of the data product itself
- Use working proficiency in SQL and Python to explore data independently and validate outcomes against business logic
- Coordinate testing, rollout and ongoing refinement of your data products based on user feedback and evolving business needs, ensuring they are genuinely adopted and used for real decisions
What success looks like
- Management and stakeholders proactively bring you into their thinking early, not because a process requires it, but because your input makes their decisions better
- Your data products are actually used and trusted for real decisions, not just delivered and shelved
- Data Engineering can build confidently because what you have handed them is clear and well scoped
What You Bring Along
Must-have
- At least 3 years of experience in an analytical, product or stakeholder-facing role
- Strong ability to understand business problems and stakeholder needs, ask the right questions to clarify intent and translate ambiguous input into clear, actionable scope and solution design
- Ability to communicate complex analytical concepts clearly to both business stakeholders and technical teams, adapting your language and level of detail to the audience
- Working proficiency in SQL and Python, sufficient to explore data independently, build prototypes and hold your own in technical conversations with Data Engineering
- Confidence working with metrics, KPIs and semantic or business definitions
- Genuine interest in applying AI tools to analytical work and comfort adopting new tools as they emerge
- A proactive, hands‑on approach: Comfortable prototyping and iterating with imperfect information and moving a topic forward rather than waiting for ideal conditions
- Excellent command of written and spoken English (the company's working language)
Nice-to-have
- Experience in or familiarity with shipping, commodity markets, macroeconomic analysis or finance
- Familiarity with Databricks, our core data platform
What We Offer
- Our corporate culture is special: relaxed and international, open and traditionally with flat hierarchies.
- Since the very beginning, teamwork and quick decision making have been our success factors. Encouraging our employees to realize their ideas and initiatives is as important to us.
If you want to know more about who we are: Oldendorff .
Please contact Stefanie Adler in case of questions.
Oldendorff Carriers does not accept paper applications for online postings.
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 are currently operating around 800 owned and chartered vessels and transporting approximately 415 million tons of cargo annually. Our global operations focus on spot business, industrial contracts, and offshore transshipment, primarily carrying iron ore, coal, and agricultural products.Our 4,500 employees from 60 countries include more than 1,000 office-based colleagues across 20 locations and over 3,000 seafarers.Our customers can expect 100% performance. All the way.
About the Role
In recent years, Oldendorff has modernised its IT landscape to meet state-of-the-art standards. Building on this foundation, we established a dedicated Data & Analytics organisation to support better decision-making across our core business areas, including Chartering, Finance, Fleet, and Operations. This round of hiring will grow the Analytics team to 18 people.
Rather than producing traditional reports, we build analytical data products that provide clear, consistent, and actionable insights, enabling better decisions and creating real business value. Our ambition goes beyond dashboards: we are moving toward AI-ready data products, modern app-style experiences, and conversational, chat-based interfaces as a core part of how our organisation will interact with data.
We aim to use data to support sustainable growth, strengthen our market leadership, and contribute to our goal of carbon neutrality by 2050.
Join Oldendorff Carriers as a Data Product Manager in Analytics and help make data-driven decision-making a genuine strength of Oldendorff. You will focus on one of our core business areas, such as Chartering, Finance, Fleet or Operations, acting as the primary link between that business and Data Engineering. The business defines the problem and the outcome it needs; you own how that outcome is achieved, from initial scoping through to real adoption in daily decision-making.
Job Responsibilities
- Act as the main analytical partner for stakeholders in areas such as Chartering, Finance, Fleet and Operations, translating business problems and desired outcomes into clearly scoped requirements and solution design, working iteratively with Data Engineering on feasibility and challenging technical constraints where they affect what gets delivered
- As part of an Analytics team that plans and prioritises together, own the roadmap across your portfolio of narrowly scoped data products and shape how work is sequenced based on business priorities
- Own and maintain the semantic layer for your data products, ensuring consistent business definitions, metrics and AI-readable context, aligned with the organisation's master data and single-source-of-truth principles
- Enable self‑service access to your data product's insights, primarily through AI‑driven tools such as conversational interfaces, natural‑language querying and proactive AI‑generated insights, so business users can get answers themselves. Where a dedicated interface is genuinely needed, prototype it yourself using AI tools to move fast and validate the concept; any prototype intended for production is handed over to Data Engineering for industrialisation once it passes defined maturity and governance criteria, while you retain ownership of the data product itself
- Use working proficiency in SQL and Python to explore data independently and validate outcomes against business logic
- Coordinate testing, rollout and ongoing refinement of your data products based on user feedback and evolving business needs, ensuring they are genuinely adopted and used for real decisions
What success looks like
- Management and stakeholders proactively bring you into their thinking early, not because a process requires it, but because your input makes their decisions better
- Your data products are actually used and trusted for real decisions, not just delivered and shelved
- Data Engineering can build confidently because what you have handed them is clear and well scoped
What You Bring Along
Must-have
- At least 3 years of experience in an analytical, product or stakeholder-facing role
- Strong ability to understand business problems and stakeholder needs, ask the right questions to clarify intent and translate ambiguous input into clear, actionable scope and solution design
- Ability to communicate complex analytical concepts clearly to both business stakeholders and technical teams, adapting your language and level of detail to the audience
- Working proficiency in SQL and Python, sufficient to explore data independently, build prototypes and hold your own in technical conversations with Data Engineering
- Confidence working with metrics, KPIs and semantic or business definitions
- Genuine interest in applying AI tools to analytical work and comfort adopting new tools as they emerge
- A proactive, hands‑on approach: Comfortable prototyping and iterating with imperfect information and moving a topic forward rather than waiting for ideal conditions
- Excellent command of written and spoken English (the company's working language)
Nice-to-have
- Experience in or familiarity with shipping, commodity markets, macroeconomic analysis or finance
- Familiarity with Databricks, our core data platform
What We Offer
- Our corporate culture is special: relaxed and international, open and traditionally with flat hierarchies.
- Since the very beginning, teamwork and quick decision making have been our success factors. Encouraging our employees to realize their ideas and initiatives is as important to us.
Interested?
If you want to know more about who we are: Oldendorff .
Please contact Stefanie Adler in case of questions.
Oldendorff Carriers does not accept paper applications for online postings.
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