Data Engineer (Azure/Snowflake)

Ascendion; Singapore; Presencial; SGD 90.000 - 130.000; Jornada completa

Descripción de la vacante

Ascendion in Singapore is seeking a Data Engineer to modernize data ingestion and migrate legacy ETL processes to MS Azure and Snowflake. This hands-on role focuses on building reliable production pipelines and reusable components that improve data delivery across the organization.

You will redesign ingestion patterns, implement batch and near-real-time pipelines, and work with data architects, analysts, and business stakeholders to deliver scalable data solutions.

Formación

• 3–6 years of data engineering experience.
• Production data pipelines on MS Azure.
• Azure Data Factory and/or Synapse Pipelines experience.
• SQL for building and troubleshooting pipelines.
• Python for data processing and pipelines.
• Data ingestion and lakehouse concepts.
• Source control and deployment processes.
• Diagnosing and resolving production data pipeline issues.
• Effective communication with technical and business stakeholders.

Responsabilidades

• Own production data ingestion solutions from design to monitoring and 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 with CDC, schema changes, retries, and backfills.
• 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 structures.
• Improve data quality and production reliability through validation, logging, monitoring, and alerting.
• Contribute to CI/CD, code reviews, security controls, and engineering standards.
• Collaborate with data architects, analysts, platform teams, and stakeholders to turn data requirements into production solutions.

Herramientas

Azure Data Factory

Synapse Pipelines

Python

SQL

Snowflake

Azure Databricks

ETL/ELT

Source Control

CI/CD

Data Ingestion

Data Lake/Lakehouse

Descripción del empleo

• 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.