DFI Retail Group Hiring! Full Time Data - Integrations Specialist in Federal Territory - Ricebowl

About Us

DFI Retail Group is a leading pan-Asian retailer and operates across four broad formats: Food (including Supermarkets, Hypermarkets, and Convenience stores), Health & Beauty, Home Furnishings, and Restaurants. The Group has operations in 12 markets and operates multiple formats in most markets to satisfy different customer segments and trades under well-recognized brands.

About The Role

We are building a unified martech ecosystem across the DFI retail group, with some markets migrating from existing platforms and others being designed and implemented from the ground up.

This role will be responsible for designing, building, and supporting scalable data pipelines that power core Martech capabilities, including customer Data platform, loyalty, campaign & journey (CMS + Marketing Automation), survey & feedback systems.

You will own real-time and batch data ingestion, data modeling, migration from legacy systems, and ongoing engineering-level change requests. Post go-live, the role provides L2 support for data pipelines, ensuring data quality, reliability, and performance, while partnering with solution architect, martech operations, and platform vendor for L3 issues and continuous improvement.

Key Responsibilities:

• Design and build scalable real-time (event-based) and batch data ingestion pipelines for customer, transactional, behavioural, and consent data across the Martech ecosystem.
• Develop and manage API, webhook, and file-based integrations across internal systems and external platforms using API gateways, cloud-based microservices, and Kubernetes environments.
• Ensure reliable and resilient data pipelines through schema validation, monitoring, error handling, replay/backfill mechanisms, fault tolerance, and SLA optimisation across multiple markets and use cases.
• Partner closely with Solution Architects and QA teams to validate end-to-end data flows supporting downstream segmentation, customer journeys, personalisation, and activation use cases.
• Design and maintain scalable data models for customer profiles, identity attributes, behavioural events, loyalty data, transactions, and consent/preference management.
• Implement efficient cloud-based storage, partitioning, and data architecture strategies to balance performance, scalability, retention, and cost across real-time and batch workloads.
• Define and enforce engineering standards and best practices across data modelling, orchestration, CI/CD, observability, governance, naming conventions, and API/event standards.
• Establish data contracts, schema registries, data quality checks, monitoring, alerting, reconciliation, and resilience mechanisms to ensure production-grade reliability and data freshness.
• Deliver post-deployment enhancements and change requests including onboarding new data sources, schema updates, performance optimisation, and new activation feeds.
• Lead data migration and onboarding activities for new markets and legacy platform transitions, including historical data loads, cutovers, validation, and post-migration support.
• Provide L2 support for data pipelines and integrations, including troubleshooting, root cause analysis, issue resolution, recovery activities, and coordination with vendors for L3 escalations.

About You

• 8+ years in data engineering with hands‑on delivery of real‑time streaming and batch pipelines
• Able to read and write in Mandarin & Cantonese for regional support
• Bachelor’s degree in engineering, computer science, or a related technical field, or equivalent hands-on experience building enterprise data and integration platforms
• Proven experience integrating via API gateways (Apigee) and microservices (Java Spring on GKE/Kubernetes).
• Proficiency in SQL and programming languages (Java, Python), with the ability to debug APIs, services, and data pipelines
• Solid experience in data modelling and storage design for customer-centric use cases, including profile unification, identity attributes, event schemas, and activation-ready datasets.
• Experience supporting platform migrations and multi-market rollouts, including historical data loads, cutover strategies, and post-migration validation
• Hands-on experience providing L2 support for data pipelines and integrations, including root cause analysis, data reprocessing, and production issue resolution.