Senior Software Engineer, Geo POI

Get to Know the Team

The POI team is part of Grab's Geo department. We build the POI data platform and key geo-services such as search, recommendation, geocoding, and reverse geocoding, supporting Grab's mobility, delivery, and payments businesses across Southeast Asia.

Get to Know the Role

We are looking for a Senior Software Engineer to join the POI team. In this role, you will own well-scoped backend systems, data pipelines, and quality workflows that improve the coverage, freshness, accuracy, and usability of map data. You will work closely with engineers, product managers, data operations, and regional stakeholders to deliver reliable POI data capabilities at scale.

Key Responsibilities

• Design, implement, and maintain backend services and data pipelines for POI ingestion, normalization, enrichment, quality validation, and publishing.
• Own medium-complexity engineering projects end to end, including technical design, implementation, testing, rollout, monitoring, and iteration.
• Improve the reliability, scalability, and observability of POI data systems, including batch and near-real-time workflows.
• Build tools and workflows that improve POI data quality, operational efficiency, and issue diagnosis across multiple markets.
• Apply Large Language Models and AI-assisted techniques to improve data parsing, entity understanding, categorization, validation, and productivity.
• Collaborate with cross-functional teams to translate business and map-quality goals into practical engineering solutions.
• Contribute to engineering best practices through code reviews, documentation, technical discussions, and mentoring interns or junior teammates when needed.

Required Engineering Skills

• Bachelor's degree or higher in Computer Science, Software Engineering, Data Science, Geographic Information Systems, or a related field.
• Solid backend development experience in at least one language such as Python, Go, or Java, with the ability to write clean, maintainable, and testable code.
• Strong understanding of computer science fundamentals, including data structures, algorithms, operating systems, computer networks, and distributed systems basics.
• Hands-on experience with databases and storage systems such as MySQL, PostgreSQL, Redis, Elasticsearch, or similar technologies.
• Experience building or maintaining data processing workflows, including data parsing, cleansing, validation, scheduling, and monitoring.
• Ability to independently analyze ambiguous problems, propose technical solutions, and drive execution with clear communication.
• Experience using AI tools such as ChatGPT, Cursor, Copilot, or similar tools to support coding, debugging, documentation, data analysis, or workflow improvement, with the ability to critically evaluate and validate AI-generated outputs.

Required POI Domain Knowledge

• Understanding of the POI data lifecycle, including source discovery, data collection, parsing, normalization, deduplication, enrichment, validation, and publishing.
• Familiarity with common POI attributes such as name, address, category, brand, location, contact information, opening hours, and source metadata.
• Understanding of POI data quality dimensions such as coverage, freshness, accuracy, completeness, duplicate rate, category precision, and user-impact measurement.
• Basic knowledge of geospatial concepts such as latitude and longitude, distance calculation, spatial indexing, geohash, S2, H3, or similar systems.
• Familiarity with search, geocoding, reverse geocoding, entity matching, or ranking concepts in location-based products.
• Ability to reason about regional and language-specific POI challenges, including address formats, local names, transliteration, category taxonomy, and market differences.
• Awareness of compliant data usage practices, source reliability, privacy considerations, and data governance in large-scale data collection and processing.

Bonus Points

• Experience with map data, POI platforms, geocoding systems, search systems, recommendation systems, or location-based services.
• Experience with large-scale data tools such as Spark, Hive, Presto, Airflow, Kafka, or similar technologies.
• Experience applying Large Language Models to data extraction, entity resolution, classification, quality checking, or workflow automation.
• Familiarity with web data collection, mobile data sources, structured and unstructured data extraction, or automation frameworks such as Scrapy, Playwright, or Selenium.
• Experience defining or improving data quality metrics, dashboards, alerts, or operational review processes.
• Ability to work with cross-market stakeholders and turn operational pain points into scalable engineering solutions.