全职 Data Engineer (Azure-Snowflake) 工作, 薪水, Ascendion 公司招聘中 - Ricebowl

Role:

• We are looking for a data engineer to help modernize our data ingestion landscape and move legacy ETL processes onto MS Azure and Snowflake.
• This is a hands-on engineering role for someone who wants to do more than maintain existing ETL jobs.
• You will help redesign ingestion patterns, build reliable production pipelines, and create reusable components that improve how data is delivered across the organization.

Responsibilities:

• Own production data ingestion solutions from design and implementation through monitoring, troubleshooting, and continuous improvement.
• Modernize legacy ETL/ELT workloads using Azure and Snowflake.
• Build batch, incremental, and near-real-time pipelines across databases, APIs, files, and event-based sources.
• Develop workflows using Azure Data Factory and/or Synapse Pipelines.
• Build reliable loading patterns covering CDC, schema changes, retries, backfills, and reprocessing.
• Develop reusable Python utilities, libraries, and ingestion components.
• Use Azure Databricks/Apache Spark where appropriate for data processing.
• Build and support Snowflake ingestion and raw-to-curated data structures.
• Improve data quality and production reliability through validation, logging, monitoring, and alerting.
• Contribute to CI/CD, code reviews, security controls, and engineering standards.
• Work closely with data architects, analysts, platform/application teams, and business stakeholders to turn data requirements into production solutions.

Required Skills:

• 3–6 years of relevant data engineering experience, or equivalent demonstrated experience.
• Building and supporting production data pipelines on MS Azure.
• Hands-on use of Azure Data Factory and/or Synapse Pipelines.
• Practical SQL experience for building and troubleshooting data pipelines.
• Python for data processing, automation, or pipeline development.
• Data ingestion and data lake/lakehouse concepts.
• Source control, code reviews, and deployment processes.
• Diagnosing and resolving production data pipeline issues.
• Communicating effectively with technical and business stakeholders.