RESPONSIBILITY
- 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 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.
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)
- Have experience with either Snowflake, Databricks or equivalent provider
MAS-EKD-19082026-1026
