Data Engineer, Paya Lebar

EXPERIENCE AND SKILLS NEEDED - Proficient in general data cleaning and transformation (e.g. SQL, pandas, R, etc) to ensure data accuracy and consistency. - Proficient in building ETL pipeline (eg. SQL Server Integration Services (SSIS), AWS Database Migration Services (DMS), Python, AWS Lambda, ECS Container task, Eventbridge, AWS Glue, Spring). - Proficient in database design and various databases (e.g. SQL, PostgreSQL, AWS S3, Athena, mongodb, postgres/gis, mysql, sqlite, voltdb, cassandra, etc). - Experience in cloud technologies such as GPC, GCC (i.e. AWS, Azure, Google Cloud). - Experience and passion for data engineering in a big data environment using Cloud platforms such as GPC, GCC (i.e. AWS, Azure, Google Cloud). - Experience with building production-grade data pipelines, ETL/ELT data integration. - Knowledge about system design, data structure and algorithms. - Familiar with data modelling, data access, and data storage infrastructure like Data Mart, Data Lake, Data Virtualisation and Data Warehouse for efficient storage and retrieval. - Familiar with rest api and web requests/protocols in general. - Familiar with big data frameworks and tools (eg. Hadoop, Spark, Kafka,RabbitMQ). - Familiar with W3C Document Object Model and customized web scraping (e.g. BeautifulSoup, CasperJS, PhantomJS, Selenium, Nodejs, etc). - Familiar with data governance policies, access control and security best practices. - Comfortable in at least one scripting language (eg. SQL,Python). - Comfortable in both windows and linux development environments. - Interest in being the bridge between engineering and analytics. Bonus Experience (Added Advantage): - Have experience building data engineering pipelines that requires integration with search indexes and is better - Have experience with Airflow and RDBMS integration and implementation (e.g. MySQL)