SDE III - Data Engineering

Glance

Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com.

InMobi

InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd.

InMobi Advertising

InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com.

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.

Since 2019, Glance has built the extraordinary. Beyond just technology. It has built a way of working.

Across 5 offices in around 3 countries, through every pivot and breakthrough, one thing has remained constant: we are builders, dreamers, doers. We are fluid enough to move fast, grounded enough to own outcomes, and human enough to care deeply about the work and about one another.

Culture is at the core of that ethos. It has been co-created by the people who built this company and by those continuing to shape it every day. It lives in how we work, how decisions are made, how teams show up for one another, and how seriously we take the responsibility to build the best.

This is a place where ambition and belonging occupy the same space. Where the bar is high and the support is real. Where freedom comes with trust, and trust comes with accountability. Where you are valued not only for what you do, but for the perspective, judgment and humanity you bring to the work. Where you will be challenged by the work and supported by the communities, passions and experiences that shape who you are beyond your role.

If this sounds like the place you have been looking for, join us.