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, catalogue 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.

The candidate will have responsibilities across the following functions :

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, personalisation, and AI applications.

- Build and maintain highly reliable ETL/ELT frameworks processing billions of events and catalogue 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 personalisation.

- Contribute to deterministic and probabilistic identity stitching frameworks.

Commerce Catalogue Platform :

- Build ingestion and enrichment pipelines for affiliate feeds, merchant catalogues, D2C integrations, and product metadata.

- Design scalable schemas and taxonomy frameworks for large and evolving commerce catalogues.

- Develop catalogue quality, deduplication, normalisation, and enrichment systems.

Feature Store and 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 optimisation, Metadata discovery, Schema change analysis, Cost optimization recommendations.

Reliability and 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 organisation.

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 Personalised Experiences : Your systems will help create a unified understanding of users across multiple surfaces, enabling highly personalised 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.

Requirements :

- 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 Modelling :

- Strong understanding of dimensional modelling, data warehousing, and large-scale schema design.

- Experience managing complex datasets and evolving schemas.

Data Quality and Observability :

Experience with :

- Data validation frameworks

- Lineage systems

- Monitoring and alerting

- Reconciliation pipelines

- CI/CD for data systems

Cloud and 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

What Success Looks Like in 12 Months :

- Built and scaled multiple production-grade pipelines powering personalisation 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 optimisation 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.