SDE III - Data Engineering

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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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

• Batch and Streaming Architectures

• Strong understanding of dimensional modeling, data warehousing, and large-scale schema design.
• Experience managing complex datasets and evolving schemas.

• Data validation frameworks
• Lineage systems
• Monitoring and alerting
• Reconciliation pipelines
• CI/CD for data systems

• GCP
• Databricks
• BigQuery
• Infrastructure as Code
• Cluster management
• Performance tuning and cost optimization

Strong programming skills in:

• Python
• Scala or Java
• SQL

Strong understanding of:

• System design
• Distributed systems
• Performance optimization
• Reliability engineering

• Product catalogs
• Affiliate commerce platforms
• Merchant feeds
• Search and recommendation systems

• Identity resolution
• Audience platforms
• Customer 360 systems
• User profiling and segmentation

• Feature stores
• Training data pipelines
• Real-time inference data systems
• MLOps infrastructure

Exposure to:

• LLM-powered developer tools
• Agents and copilots
• Metadata intelligence systems
• Automated debugging and remediation workflows

• 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.

• 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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