1. Data Warehouse
Data Engineer will help data scientist, analyst and data product owner in their effort to meet analytic business needs and to create better data products. A data engineer explores data, designs data model, builds data pipeline to ingest and organize data into Data Warehouse, integrates disparate data sources, moves data from source to target systems, creates custom extracts for external vendors, designs and creates physical and logical data models which includes normalizing and conforming data using industry or business specific standards. They are also responsible for data quality assurance.
- Design, implement, maintain data models with Ralph Kimball’s Dimensional Modelling methodology.
- Build and maintain data pipelines using tools such as NIFI, Airflow, etc.
- Evaluate and define KPIs to monitor and manage data quality.
- Support data scientists, data analyst to explore data in Data warehouse.
- Build and maintain integration data flow provided to other departments.
- Maintain update-to-date documentation of data assets, ETL flows, data dictionary.
- Perform other duties, tasks, or special assignements that relates to their skillsets as needed.
2. Data Platform
The data platform engineers are responsible for providing the core infrastructure and data platforms that enable product and data warehouse engineers to efficiently build and operate scalable, reliable analytics applications. They work closely with the larger group infrastructure team, the backend developers, the business intelligent team, data scientists, to provide a cohesive set of foundational technology that enable 1MG to become a world-class, data-powered organization.
- Design, build and deploy many Big Data applications within a streaming architecture to ingest, transform, and store data to be used in Machine Learning Models, developing prototypes quickly.
- Build and deploy the platforms for Data Monitoring, Data Catalog, Data Governance, Self-service Analytics.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
