This is a remote position.
Job title: Data Engineer
Schedule: Full time; 40 hours per week; Monday to Friday
Rate:Philippine Pesos per month
We are seeking a highly skilled and experienced Data Engineer to join our Data Engineering team. The ideal candidate will have a strong foundation in data processing and analytics with specific expertise in Microsoft Azures Synapse Analytics and Azure Data Factory. You will play a crucial role in developing and optimizing our data pipeline architecture as well as managing and improving data flow and collection for crossfunctional teams.
Responsibilities:
- Design build and maintain scalable and reliable data pipelines using Azure Synapse Analytics and Azure Data Factory.
- Work closely with stakeholders to understand data requirements and implement systems for largescale data analysis and reporting.
- Ensure data quality and consistency across data pipelines and implement secure and compliant data handling procedures.
- Collaborate with data scientists and analysts to support data modelling analysis and machine learning projects.
- Monitor and optimize performance of data pipelines and databases.
Requirements
- Bachelor s degree in computer science Engineering Mathematics or a related field (Experience > Education)
- Minimum of 3 years of experience in a data engineering role with a strong focus on Microsoft Azure data services particularly Azure Synapse Analytics and Azure Data Factory.
- Proven expertise in SQL Python and other scripting languages.
- Experience with data modeling ETL processes and data warehousing principles.
- Familiarity with Azure Databricks Azure Data Lake Storage and other components of the Azure data ecosystem is a plus.
- Strong analytical and problemsolving skills with the ability to work independently and as part of a team.
- Excellent communication skills with the ability to convey complex technical information to nontechnical stakeholders.
- PowerBI experience is also advantageous.
Benefits
- Permanent work from home
- AU morning shift
- Fun and engaging culture
data engineer, data engineering, etl, data modeling