Staff Software Engineer - Maps

The Places Data Team owns Uber ground truth places dataset (POI, Addresses, Building Footprints, Entrances) powering the core of any trip - pick-up and drop-off locations.

Working on massive scale the team is responsible for:

• Conflation of POI and address data from dozens providers into an unambiguous stable dataset, solving graph connectivity problems on a scale of dozens billions of edges, using ML for matching and summarization.
• Data inference, use all available signals from users, trips, providers to find missing places or incorrect attributes at scale.Ground truth data - inference and conflation of building footprints, entrances from provider, street level imagery, etc.

What you will do

• Design, build, and maintain data pipelines that consumes and conflate POI/Address/BFP data from multiple providers;
• Lead technically complex initiatives such transformation flat data structure into graph, connecting all spatial data together, places inference, aliases of POIs, data A/B experimentation, etc.
• Navigate the trade-offs between short-term tactical fixes and long-term architectural stability while keeping our Maps ecosystem running smoothly.
• Own your work end-to-end, from drafting the multi-year technical vision for traffic domains to debugging production issues when the stakes are high.
• Collaborate cross-functionally with Data Scientists, Product Managers, and Engineering peers to translate complex business needs into robust, scalable software.
• Champion engineering best practices like code health and design clarity, even when the pace is fast and priorities shift.Mentor and unblock other ICs on the team, raising the bar for technical excellence through thoughtful design reviews and leadership by example.

Do you have experience in Spark?, Do you have a Master's degree?, * Minimum of eight years of professional experience in software engineering.

• Bachelor's or Master's degree in Computer Science, a related technical field, or an equivalent level of practical experience.
• Proficiency in programming with Go, Python, Java, or C++.
• Strong foundation in computer science fundamentals, including data structures, algorithms, complexity analysis, and a systematic approach to troubleshooting.
• Prior experience in technical leadership roles.Excellent interpersonal and communication skills with the ability to collaborate effectively across teams and with various stakeholders., * 8+ years of experience designing and operating large-scale data pipelines, preferably in a geospatial, mapping, or location intelligence domain.
• Deep familiarity with geospatial data formats and concepts (POI, address data, building footprints, GIS systems, spatial indexing such as H3/S2/Geohash).
• Hands-on experience with data conflation, entity resolution, or record linkage - merging data from multiple heterogeneous providers into a unified, high-quality dataset.
• Experience applying machine learning techniques (e.g., embedding models, classification, clustering) to data matching, deduplication, or attribute summarization at scale.
• Strong background in large-scale graph data modeling and processing - familiarity with graph connectivity problems, graph databases, or distributed graph computation frameworks.
• Proficiency with distributed data processing frameworks such as Apache Spark
• Demonstrated ability to own and drive multi-year technical roadmaps, including authoring technical vision documents and architectural design proposals.
• Experience designing and running data A/B experimentation frameworks, including metric definition, experiment instrumentation, and statistical evaluation of data quality changes.
• Familiarity with data quality frameworks and observability tooling - monitoring pipeline health, data freshness, coverage, and accuracy at production scale.
• Prior experience mentoring senior and mid-level engineers, conducting design reviews, and elevating engineering standards across a team.
• Track record of effective cross-functional collaboration with Data Scientists, Product Managers, and partner engineering teams to translate ambiguous business requirements into concrete technical solutions.