Data Engineer, Asia Pacific

Company description:

SSP is a global leading operator of food and beverage outlets in travel locations employing 49,000 colleagues in around 3,000 units across nearly 40 countries. We specialise in designing, creating and operating a diverse range of food and drink outlets in airports, train stations and other travel hubs across six formats: sit-down and quick service restaurants, bars, cafés, lounges, and food-led convenience stores. Our extensive portfolio of brands features a mix of international, national, and local brands, tailored to meet the diverse needs of our clients and customers. Our SSP Asia Pacific journey started in Thailand over 25 years ago. Since then, we have grown to include Singapore, Hong Kong SAR, Australia, New Zealand, Philippines, Malaysia and Indonesia with more than 6,000 colleagues, 150+ brands and 300+ units. Our purpose is to be the best part of the journey, and our focus is on making every journey taste better - bringing great food and welcoming hospitality to travellers across the globe. Sustainability is crucial for our long-term success, and we aim to deliver positive impact for our business while uniting stakeholders to promote a sustainable food travel sector. Our people are at the heart of our business and our values are integral to our business, underpinning everything we do.

Job description:

Data Engineer (Azure Databricks) | 1-Year Contract

We are looking for a hands-on, execution-driven Data Engineer to join our team on a contract basis. In this role, you will work directly alongside our Lead Data Engineer and Analytics Engineers to build and maintain scalable cloud data pipelines, establishing robust Bronze and Silver layers within our Medallion Architecture to support strategic BI initiatives (Power BI & Sigma). This position offers a balanced split between new pipeline engineering, quality assurance, operational troubleshooting, and ad-hoc data support.

Key Responsibilities & Work Allocation

Pipeline Engineering & QA (60%)

• Design, build, and deploy ETL/ELT pipelines on Azure Databricks.
• Construct and optimize Bronze (Raw Ingestion) and Silver (Cleaned & Conformed) data layers.
• Conduct rigorous Data Quality QA, implementing automated testing frameworks to ensure data accuracy and consistency before handoff to Analytics Engineers.

Pipeline Debugging & Maintenance (30%)

• Monitor, troubleshoot, and optimize existing Azure Databricks workflows and legacy data jobs.
• Resolve pipeline failures, manage data schema drift, and optimize PySpark query performance to meet strict SLAs.

Ad-hoc Analysis & Stakeholder Support (10%)

• Conduct root-cause analysis on data discrepancies and support immediate business queries.
• Collaborate with Analytics Engineers to ensure seamless downstream modeling (Gold layer / Data Marts) for Power BI and Sigma.

Technical Qualifications & Experience
Must-Have

• 2-4 years of hands-on Data Engineering experience in building and operating production-grade cloud data pipelines.
• Hands-on proficiency with Azure Databricks, PySpark, and Spark SQL.
• Demonstrated experience constructing Bronze and Silver layers using Medallion architectures.
• Strong SQL skills (complex transformations, window functions, and performance tuning).
• Solid understanding of data pipeline testing, QA methodologies, and automated data validation.
• Familiarity with supporting BI tools such as Power BI or Sigma.
• Experience with Git and standard version control/CI/CD practices.

Nice-to-Have

• Experience working alongside Analytics Engineers using dbt (data build tool).
• Exposure to cloud orchestration tools like Apache Airflow.
  Exposure to core cloud data services across Azure, AWS, or GCP (e.g., ADLS Gen2, S3, or GCS).