AWS Data Engineer

• Design, develop and deploy data tables, views and marts in data warehouses, operational data store, data lake and data virtualization.

• Perform data extraction, cleaning, transformation, and flow. Web scraping may be also a part of the work scope in data extraction.

• Design, build, launch and maintain efficient and reliable large-scale batch and real-time data pipelines with data processing frameworks.

• Integrate and collate data silos in a manner which is both scalable and compliant.

• Collaborate with Project Manager, Data Architect, Business Analysts, Frontend Developers, Designers and Data Analyst to build scalable data[1]driven products.

• Be responsible for developing backend APIs & working on databases to support the applications.

• Work in an Agile Environment that practices Continuous Integration and Delivery.

• Work closely with fellow developers through pair programming and code review process.

• 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 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.