Data Engineer (Big Data)

About the role

SavourTech Labs is a Singapore-based food-tech analytics firm working with hawker groups, café chains and hotel F&B teams to deliver real-time insights and forecasting. We ingest high-volume POS, inventory and delivery feeds to support pricing, labour planning and demand forecasting across multiple outlets.

The Data Engineer (Big Data) will design and maintain ETL/ELT pipelines, manage our data lake, and optimise batch and streaming workloads so our analytics and ML models run reliably. You will work closely with product managers, data scientists and operations teams — often translating business needs (e.g., peak-hour demand, stockouts, multi-outlet menu changes) into reproducible data products.

This is a hybrid role based in central Singapore with occasional visits to partner kitchens and client sites (hawker centres, heartland malls, hotel back-of-house). The role is ideal for someone who enjoys building production-grade pipelines, cares about data quality, and wants to see direct results in F&B operations and revenue metrics.

About SavourTech Labs

SavourTech Labs provides analytics, forecasting and data platform services exclusively for the food & beverage sector. Our clients include multi-outlet cafés, casual restaurants, cloud kitchens and hotel F&B teams. We focus on rapid deployment, practical dashboards and models that drive margin and reduce wastage.

What you can expect

• Food-tech specialist with stable client base across F&B and hospitality
• Hybrid working with central office near Raffles Place
• Hands-on ownership of data platform and visibility to senior leadership
• Opportunities to work directly with restaurant operations and influence product roadmap

Key responsibilities

• Design, build and maintain scalable ETL/ELT pipelines for batch and streaming data (Kafka, Spark, or equivalent).
• Ingest and normalise POS, delivery platform, inventory and third-party datasets into the data lake.
• Optimise large-scale Spark jobs and ensure pipelines meet SLA for freshness and accuracy.
• Implement data quality checks, monitoring and alerting; respond to production incidents and on-call rotations.
• Collaborate with data scientists to productionise ML features and model inference pipelines.
• Work with product and operations to translate business requirements (demand forecasting, menu performance) into data solutions.
• Manage schema evolution, data partitioning and cost-efficient storage in cloud (S3 / GCS) or on-prem alternatives.
• Document data contracts, lineage and ensure compliance with PDPA and client data access policies.
• Mentor junior engineers and contribute to best practices around testing, CI/CD and deployment.

Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science or equivalent practical experience.
• 3+ years experience building production data pipelines using Spark, Flink or similar big-data frameworks.
• Strong programming skills in Python and/or Scala; solid SQL expertise for complex analytics queries.
• Experience with streaming and messaging systems (Kafka, Pub/Sub) and orchestration tools (Airflow, Prefect).
• Hands-on experience with cloud platforms (AWS, GCP or Azure) and cloud-native data services.
• Familiarity with data lakehouse or warehouse patterns (e.g., Delta Lake, Iceberg, BigQuery, Redshift).
• Experience with containerisation (Docker), Kubernetes and CI/CD for data workloads.
• Good understanding of data governance, security and Singapore PDPA considerations.
• Ability to work hybrid model and attend occasional client visits; flexible for limited on-call support.

How to apply

Send your CV and a brief note about your relevant big-data and F&B experience to the email below. Include links to any public code repos or project summaries if available.