Total 3 data entry clerk job vacancies in Singapore

Responsibilities:

• Design, develop, and deploy data tables, views, and marts in data warehouses, operational data stores, data lakes, and data virtualization platforms.
• Perform data extraction, cleaning, transformation, and flow, including web scraping as required.
• Build, launch, and maintain efficient, reliable large-scale batch and real-time data pipelines using data processing frameworks.
• Collaborate with cross-functional teams, including Project Managers, Data Architects, Business Analysts, and others, to develop scalable data-driven products.
• Develop backend APIs and manage databases to support applications.
• Work in an Agile environment, practicing Continuous Integration and Delivery (CI/CD).

Experience and Skills Required:

• Proficiency in data cleaning and transformation tools (e.g., SQL, pandas, R).
• Expertise in building ETL pipelines using tools such as SQL Server Integration Services (SSIS), AWS Database Migration Services (DMS), Python, and AWS Lambda.
• Strong database design skills and experience with various databases (e.g., SQL, PostgreSQL, MongoDB, MySQL).
• Familiarity with cloud technologies (AWS, Azure, Google Cloud).
• Knowledge of system design, data structures, and algorithms.
• Proficiency in scripting languages (e.g., SQL, Python).

Responsibilities:

• Design, develop, and deploy data tables, views, and marts in data warehouses, operational data stores, data lakes, and data virtualization platforms.
• Perform data extraction, cleaning, transformation, and flow, including web scraping as required.
• Build, launch, and maintain efficient, reliable large-scale batch and real-time data pipelines using data processing frameworks.
• Integrate and consolidate data silos in a scalable and compliant manner.
• Collaborate with cross-functional teams, including Project Managers, Data Architects, Business Analysts, and others, to develop scalable data-driven products.
• Develop backend APIs and manage databases to support applications.
• Participate in pair programming and code reviews with fellow developers.

Experience and Skills Required:

• Proficiency in data cleaning and transformation tools (e.g., SQL, pandas, R).
• Expertise in building ETL pipelines using tools such as SQL Server Integration Services (SSIS), AWS Database Migration Services (DMS), Python, AWS Lambda, and ECS Container tasks.
• Strong database design skills and experience with various databases (e.g., SQL, PostgreSQL, MongoDB, MySQL).
• Experience with cloud technologies (AWS, Azure, Google Cloud).
• Familiarity with big data frameworks and tools (e.g., Hadoop, Spark, Kafka).
• Proficiency in scripting languages (e.g., SQL, Python).

Responsibilities:

• Design, develop, and deploy data tables, views, and marts in data warehouses, operational data stores, data lakes, and data virtualization platforms.
• Perform data extraction, cleaning, transformation, and flow, including web scraping as required.
• Build, launch, and maintain efficient, reliable large-scale batch and real-time data pipelines using data processing frameworks.
• Integrate and consolidate data silos in a scalable and compliant manner.
• Collaborate with cross-functional teams, including Project Managers, Data Architects, Business Analysts, and others, to develop scalable data-driven products.
• Develop backend APIs and manage databases to support applications.
• Work closely with fellow developers through pair programming and code reviews.
• Lead the design and implementation of data solutions in a big data environment.

Experience and Skills Required:

• Proficiency in data cleaning and transformation tools (e.g., SQL, pandas, R).
• Expertise in building ETL pipelines using tools such as SQL Server Integration Services (SSIS), AWS Database Migration Services (DMS), Python, AWS Lambda, and ECS Container tasks.
• Strong database design skills and experience with various databases (e.g., SQL, PostgreSQL, MongoDB, MySQL).
• Experience with cloud technologies (AWS, Azure, Google Cloud).
• Familiarity with big data frameworks and tools (e.g., Hadoop, Spark, Kafka).
• Knowledge of system design, data structures, and algorithms.
• Proficiency in scripting languages (e.g., SQL, Python).
• Leadership experience in data engineering projects.