Data Engineer (Azure/Snowflake)

Collabera Digital
Singapore, Singapore
Salary not specified
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Job Description

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.

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