As a Data Engineer, you will support implementation of projects focused on collecting, aggregating, storing, reconciling, and making data accessible from disparate sources to enable analysis and decision making. This role will also play a critical part of the data supply chain, by ensuring stakeholders can access and manipulate data for routine and ad hoc analysis. Additionally, you will support the full lifecycle of data from ingesting through analytics to action.
- Support planning and delivery of data warehouse and storage architecture.
- Support the planning and implementation of data design services, providing sizing and configuration assistance, and performing needs assessments.
- Develop and maintain data warehouse schematics, layouts, architectures, and relational databases for data storage and data mining
- Deliver data to end users using SSIS, SSRS, Excel, PowerPivot and SharePoint Performance Point
- Customize data storage and extraction, data mining, database architecture, metadata and repository creation.
- Implement effective metrics and monitoring processes.
- Participate in the design and development of business intelligence reporting tools.
- Program and maintain report forms and formats, information dashboards, data generators, canned reports and other end-user information portals or resources.
- Knowledge of database, storage, collection and aggregation models, techniques and technologies, with the ability to apply such methods to solve business problems.
- Knowledge of structured problem solving assignments and strong project management and people management skills
- Knowledge of SharePoint, PowerPivot, SRSS, Excel skills (with embedded Pivot Tables & Macros) with SQL Skills
- A Bachelor’s degree in Applied Mathmatics, Statistics, or another relevant field. An equivalent combination of education and experience will also suffice.
- About 1-2+ years’ relevant professional experience.
How can we help you?
Contact us at the Probyto office nearest to you or submit a business inquiry online.
2019 is year of making Data Science (AI/ML) more accessible, RoI Driven and faster to market. Empowering organisations with Data Solutions at scale should be the focus of industry.