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SSP

Data Engineer, Asia Pacific

SSP Hong Kong, China

Food and Beverage Services · 10,001+ employees

14 h ago
data-engineer Mid (2-5 yrs) Contractor China
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About the role

The Data Engineer will design, build, and deploy scalable ETL/ELT pipelines using Azure Databricks within a Medallion Architecture. They are also responsible for monitoring, troubleshooting existing workflows, and ensuring data quality through rigorous testing.

What they look for

Azure Databricks PySpark Spark SQL Medallion Architecture SQL Data Engineering ETL/ELT Data Quality QA Power BI Sigma Git CI/CD Data Pipeline Development Troubleshooting

Requirements

Candidates must have 2–4 years of hands-on experience in building production-grade cloud data pipelines and proficiency in Azure Databricks, PySpark, and SQL. Strong knowledge of data pipeline testing methodologies and version control practices is required.

Full description

About the Role

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).

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