Glance
Glance AI is an AI commerce platform shaping the next wave of e-commerce with inspiration-led shopping, less about searching for what you want and more about discovering who you could be. Operating in 140 countries, Glance AI transforms every screen into a stage for instant, personal, and joyful discovery, where inspiration becomes something you can explore, feel, and shop in the moment. Its proprietary models, seamlessly integrated with Google’s most advanced AI platforms, Gemini and Imagen on Vertex AI, deliver hyper-realistic, deeply personal shopping experiences across categories such as fashion, beauty, travel, accessories, home décor, pets, and more. Designed to seamlessly integrate into everyday consumer technology, Glance AI reimagines the future of e-commerce with inspiration-led discovery and shopping. With an open architecture built for effortless adoption across hardware and software ecosystems, Glance AI is creating a platform that can become a staple in everyday consumer technology. It partners with the world’s leading smartphone makers, connected TV manufacturers, telecom providers, and global brands — meeting people where they are: on mobile, smart TVs, and brand websites. Through Glance AI’s rich first-party data and unparalleled consumer access, it harnesses InMobi’s global scale, insights, and targeting capabilities to create high-impact, performance-driven shopping journeys for brands worldwide. Part of the InMobi Group, a global technology and advertising leader reaching over 2 billion devices and serving more than 30,000 enterprise brands worldwide, Glance AI is backed by Google, Jio Platforms, and Mithril Capital. SDE-3 – Data Engineering and Capabilities Team About the Team At Glance, we are building a first-of-its-kind generative AI-powered commerce platform that transforms how users discover and shop across mobile surfaces, TV, and brand stores. The Data Engineering & Capabilities Team is responsible for building the intelligence backbone that powers personalization, recommendations, multimodal search, experimentation, analytics, and AI-driven commerce experiences. We bring together data from affiliate feeds, OEM integrations, commerce catalogs, user interactions, and transaction systems to create trusted, scalable, and ML-ready data products. As an SDE-3 Data Engineer, you will play a key role in building and scaling the foundational data systems that power Glance's next generation of AI products. Role Overview We are looking for a highly skilled and hands-on Data Engineer to own the design, development, and operation of large-scale data platforms and capabilities. You will work closely with Applied Scientists, ML Engineers, Product Managers, Analytics teams, and Platform Engineers to build reliable data pipelines, feature stores, identity systems, catalog infrastructure, and self-service capabilities that accelerate experimentation and machine learning development. You will be expected to independently drive complex technical initiatives from design through production while maintaining high standards of quality, scalability, and operational excellence. Key Responsibilities Data Platform Development Design and build scalable batch and real-time data pipelines using Spark, Flink, Kafka, and Airflow. Develop data products that support analytics, experimentation, recommendation systems, personalization, and AI applications. Build and maintain highly reliable ETL/ELT frameworks processing billions of events and catalog updates. User Data Platform Develop systems for user identity resolution and cross-surface signal aggregation across Mobile, TV, OEM, and Commerce ecosystems. Build datasets and services that support user profiling, audience creation, segmentation, and personalization. Contribute to deterministic and probabilistic identity stitching frameworks. Commerce Catalog Platform Build ingestion and enrichment pipelines for affiliate feeds, merchant catalogs, D2C integrations, and product metadata. Design scalable schemas and taxonomy frameworks for large and evolving commerce catalogs. Develop catalog quality, deduplication, normalization, and enrichment systems. Feature Store & ML Enablement Build reusable feature generation frameworks for ML and recommendation systems. Create low-latency feature pipelines serving training and online inference workloads. Partner with Applied Scientists to improve feature discoverability, governance, and reusability. AI-Powered Engineering Capabilities Develop internal AI-powered tools, agents, and self-service platforms that improve developer productivity. Build solutions for: Pipeline debugging Data quality triage SQL generation and optimization Metadata discovery Schema change analysis Cost optimization recommendations Reliability & Operational Excellence Own production services and pipelines with strong SLAs. Build observability into every layer through monitoring, lineage, alerting, reconciliation, and quality checks. Participate in incident response, root-cause analysis, and operational reviews. Continuously improve platform reliability, performance, and cost efficiency. Technical Leadership Lead architecture and design discussions for critical platform components. Drive engineering best practices around code quality, testing, documentation, CI/CD, and infrastructure management. Mentor junior engineers and contribute to raising the technical bar across the organization. Impact You Will Make Accelerate AI Innovation - You will enable faster experimentation and model deployment by building trusted, reusable data assets and feature pipelines. Power Personalized Experiences - Your systems will help create a unified understanding of users across multiple surfaces, enabling highly personalized commerce experiences. Improve Platform Reliability - You will build observability-first infrastructure that ensures data quality, lineage, and trust across the ecosystem. Scale Commerce Intelligence - Your work will transform fragmented commerce and engagement signals into a strategic advantage for Glance's AI-powered commerce platform. Increase Engineering Velocity - Through automation, self-service capabilities, and AI-assisted workflows, you will reduce operational overhead and accelerate development cycles. Experience & Requirements Required Qualifications Experience 6–10 years of experience in Data Engineering, Distributed Systems, or Data Platform development. Strong experience owning large-scale production systems end-to-end. Data Engineering Expertise: Strong hands-on experience with: Apache Spark Kafka Flink Airflow Distributed Data Processing Batch and Streaming Architectures Data Modeling Strong understanding of dimensional modeling, data warehousing, and large-scale schema design. Experience managing complex datasets and evolving schemas. Data Quality & Observability Experience with: Data validation frameworks Lineage systems Monitoring and alerting Reconciliation pipelines CI/CD for data systems Cloud & Platform Engineering Experience with: GCP Databricks BigQuery Infrastructure as Code Cluster management Performance tuning and cost optimization Software Engineering Strong programming skills in: Python Scala or Java SQL Strong understanding of: System design Distributed systems Performance optimization Reliability engineering Preferred Qualifications Commerce Domain Experience Experience working with: Product catalogs Affiliate commerce platforms Merchant feeds Search and recommendation systems Identity & Personalization Experience with: Identity resolution Audience platforms Customer 360 systems User profiling and segmentation Feature Stores & ML Platforms Experience building: Feature stores Training data pipelines Real-time inference data systems MLOps infrastructure AI-Assisted Engineering Exposure to: LLM-powered developer tools Agents and copilots Metadata intelligence systems Automated debugging and remediation workflows What Success Looks Like in 12 Months Built and scaled multiple production-grade pipelines powering personalization and commerce intelligence. Reduced data quality incidents through automated observability and reconciliation frameworks. Delivered reusable feature generation capabilities adopted by Applied Science teams. Improved platform efficiency through workload optimization and infrastructure cost reduction. Developed self-service capabilities that significantly improve productivity for data consumers and ML teams. Become a go-to technical leader for large-scale data platform initiatives. What This Role Is Not Not a pure ETL developer role focused only on pipeline implementation. Not a pure platform operations role. Not a people-management role. This is a senior hands-on engineering role where you will design, build, operate, and influence the core data infrastructure that powers Glance's AI and commerce ecosystem. You are expected to own critical systems, drive technical decisions, and deliver business impact through engineering excellence. 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