Ascendion
Data Engineer (Azure/Snowflake)
Designs and builds modern data ingestion pipelines on Azure/Snowflake, migrating legacy ETL processes to scalable, reusable components for real-time and batch data processing.
- Compensation
- Not disclosed
- Location
- Singapore, Singapore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3–6 years of relevant data engineering 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
Full description
- 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.