- Take part in designing, developing and testing ETL applications or pipelines
- Write well-designed, efficient code that is easily understood by fellow developers
- Troubleshoot, debug, and enhance existing applications
- Collaborate with product owners, designers, and other developers in an Agile environment
- Participate in the development and support of multiple products
- Lead and mentor less experienced developers
- Communicate clearly and work with non-technical stakeholders
- Support or collaborate with application developers, database architects, data analysts and data scientists to ensure optimal data delivery architecture throughout ongoing projects/operations.
- Design, build, and manage analytics infrastructure that can be utilized by data analysts, data scientists, and non-technical data consumers, which enables functions of the big data platform for Analytics.
- Develop, construct, test, and maintain architectures, such as databases and large-scale processing systems that help analyze and process data in the way the Analytics organization requires.
- Develop highly scalable data management interfaces, as well as software components by employing programming languages and tools.
- Work closely with a team of Data Science staff to take existing or new models and convert them into scalable analytical solutions.
- Design, document, build, test and deploy data pipelines that assemble large complex datasets from various sources and integrate them into a unified view.
- Identify, design, and implement operational improvements: automating manual processes, data quality checks, error handling and recovery, re-designing infrastructure as needed.
- Create data models that will allow analytics and business teams to derive insights about customer behaviors
- Build new data pipelines, identify existing data gaps and provide automated solutions to deliver analytical capabilities and enriched data to applications.
- Responsible for obtaining data from the System of Record and establishing batch or real-time data feed to provide analysis in an automated fashion.
- Develop techniques supporting trending and analytic decision making processes
- Apply technologies for responsive front-end experience
- Ensure systems meet business requirements and industry practices
- Research opportunities for data acquisition and new uses for existing data
- Develop data set processes for data modeling, mining and production
- Integrate data management technologies and software engineering tools into existing structures
- Employ a variety of languages and tools (e.g. scripting languages)
Must Have Technologies:
- AWS S3
Nice To Have Technologies:
- AWS services
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