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全职 Data Engineer, Asia Pacific 工作, 薪水, SSP Hong Kong 公司招聘中 - Ricebowl

Data Engineer, Asia Pacific

SSP

Undisclosed

全职

Hong Kong

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工作地点

  • Hong Kong Hong Kong

职位描述

岗位职责

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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