Job Description
About The Role and What You'll Need
About the Role
At Uber, we are reimagining the way the world moves. In EMEA BizOps, we are extending that ambition to how we move Gross Bookings, improve EBITDA, and scale better decision-making across the region.
BizOps sits at the center of Uber's regional operating system. We help teams understand performance, identify opportunities, make better tradeoffs, and turn insight into action across EMEA. As Uber grows, the volume, complexity, and importance of our data continues to grow with it. Our stakeholders need trusted metrics, reliable data products, and intuitive tools that help them understand the business faster and act with confidence.
We are looking for an Analytics Data Engineer to join our team and help build the analytics foundation for EMEA BizOps. You will partner with Operations, BizOps, Data Science, Product, Engineering, and Finance stakeholders to transform raw and complex data into reliable metrics, canonical datasets, dashboards, and self-serve tools.
This is a hands-on builder role for someone who loves solving business problems with high-quality data systems. You will write pipelines, build data models, add new metrics to Uber's centralized data warehouse, create visualizations, and help teams access the data they need for reporting, models, experiments, and operational decision-making.
You should be excited by ambiguity, energized by stakeholders, and motivated by impact. The right person is a go-getter: highly engaged, technically strong, curious about the business, and passionate about making data easier to use at scale.
What the Candidate Will Need / Bonus Points
- What the Candidate Will Do -
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Understand the data needs of stakeholder teams across EMEA BizOps, Operations, Data Science, Product, Engineering, and Finance, and translate those needs into clear technical requirements.
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Define, build, and manage reliable data pipelines that transform raw and complex data into trusted, reusable datasets.
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Add new metrics, dimensions, and levels of granularity to Uber's centralized data warehouse, helping ensure teams use consistent definitions to measure business quality and performance.
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Build and maintain canonical datasets, modeled tables, and metric layers that make it easier for teams to answer recurring business questions without manual work.
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Establish high standards for data integrity, reliability, documentation, and timeliness, ensuring stakeholders can trust the data products they use.
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Develop insightful, reliable dashboards and visualizations that track core business metrics, surface exceptions, and help teams manage performance more effectively.
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Build foundational data products, dashboards, and tools that enable self-serve analytics and reduce dependency on ad hoc reporting.
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Partner with Data Scientists to make modeling, experimentation, diagnostics, and analysis easier by improving access to clean, well-structured data.
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Work directly with senior operations & business stakeholders to clarify business questions, challenge unclear metric definitions, and turn ambiguous needs into scalable solutions.
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Champion manage by exception" ways of working by helping teams define the metrics, thresholds, diagnostics, and tooling needed to focus attention where it matters most.
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Influence the future roadmap of EMEA BizOps data products from a data systems perspective, identifying where better pipelines, metrics, tooling, or visualizations can unlock better decisions.
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Become an expert in Uber's business data, data models, metric definitions, and regional operating context.
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Mentor and support others in analytics, data engineering, data modeling, dashboard design, and data product best practices.
What Success Looks Like
In this role, success means teams across EMEA can access the metrics, datasets, dashboards, and tools they need without relying on manual analysis or one-off reporting. Stakeholders trust the numbers. Data Scientists can find clean inputs for models and experiments. Operations teams can diagnose performance faster. BizOps can focus more time on decisions and less time reconciling data.
You will help build the data foundation that lets Uber EMEA move faster, operate smarter, and make better decisions at scale.
- Basic Qualifications -
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5+ years of experience in analytics engineering, data engineering, business intelligence engineering, data product, or quantitative analytics roles.
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Advanced SQL skills with strong experience building reliable data models, production-grade datasets, and scalable data pipelines.
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Hands-on experience with Python (or similar) and modern data development practices, including orchestration, testing, version control, documentation, and data quality management.
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Experience building dashboards, self-serve analytics tools, or other data products used by cross-functional stakeholders.
- Strong analytical, communication, and stakeholder management skills, with a demonstrated ability to translate ambiguous business problems into trusted data solutions that drive decision-making.
- Preferred Qualifications -
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Experience with data engineering stacks and pipeline tools such as Airflow, Spark, Presto, or comparable technologies
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Background in complex, data-driven businesses such as marketplaces, mobility, delivery, fintech, finance, or other high-scale operational environments.
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Expertise in defining and governing business metrics, KPI frameworks, semantic layers, or centralized metric taxonomies across multiple teams.
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Proven partnership with Product, Engineering, Data Science, Operations, or Finance teams to build analytics tooling, data products, and self-serve capabilities.
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Strong analytical toolkit, including experimentation, forecasting, marketplace dynamics, causal inference, operational diagnostics, or quantitative business analysis.
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Familiarity with modern AI-enabled tooling and data products that help teams identify exceptions, diagnose root causes, and accelerate decision-making at scale.
