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 maybe also a part of the work scope in data extraction.

-Design, build, launch and maintain efficient and reliable large-scale batch andreal-time data pipelines with data processing frameworks. -Integrate and collate data silos in a manner which is both scalable andcompliant.

-Collaborate with Project Manager, Data Architect, Business Analysts, FrontendDevelopers, Designers and Data Analyst to build scalable data driven products. - Beresponsible for developing backend APIs & working on databases to supportthe applications.

- Workin an Agile Environment that practices Continuous Integration and Delivery. - Workclosely with fellow developers through pair programming and code reviewprocess.

The teamis expected to perform Data Warehousing tasks, mainly in AWS GCC, and manageAPIs Qualifications -Proficient in general data cleaning and transformation (e. g. SQL, pandas, R,etc) to ensure data accuracy and consistency. -Proficient in building ETL pipeline (e. g.

SQL Server Integration Services -(SSIS), AWS Database Migration Services (DMS), Python, AWS Lambda, ECSContainer task, Eventbridge, AWS Glue, Spring). -Proficient in database design and various databases (e. g. SQL, PostgreSQL, AWSS3, Athena, MongoDB, postgres/gis, MySQL, SQLite, voltdb, Cassandra, etc).

-Experience in cloud technologies such as GPC, GCC (i. e. AWS, Azure, GoogleCloud). -Experience and passion for data engineering in a big data environment usingCloud platforms such as GPC, GCC (i. e. AWS, Azure, Google Cloud). -Experience with building production-grade data pipelines, ETL/ELT dataintegration.

-Knowledge about system design, data structure and algorithms. -Familiar with data modelling, data access, and data storage infrastructure likeData Mart, Data Lake, Data Virtualisation and Da