Data Engineer II, Time and Pay Innovation
Are you passionate about data and code? Does the prospect of dealing with massive volumes of data excite you? Do you want to build data engineering solutions that process billions of records and supports handling of translations data using AWS technologies?
As a Data Engineer, you would own the architecture of DW solutions for the Enterprise using multiple platforms. You would have the opportunity to lead the design, creation and management of extremely large datasets working backwards from business use case.You would use your strong business and communication skills to be able to work with business analysts and engineers to determine how best to design the data warehouse for reporting and analytics. You will be responsible for designing and implementing scalable ETL processes in the data warehouse platform to support the rapidly growing and dynamic business demand for data, and use it to deliver the data as service which will have an immediate influence on day-to-day decision making.
Key job responsibilities- Develop data products, infrastructure and data pipelines leveraging AWS services (such as Redshift, Kinesis, EMR, Lambda etc.) and internal BDT tools (Datanet, Cradle, QuickSight etc.
- Improve existing solutions and come up with next generation Data Architecture to improve scale, quality, timeliness, coverage, monitoring and security.
- Develop new data models and end to data pipelines.
- Lead design reviews for other Data Engineers on the team and actively participate in design reviews of related tech products.
- Provide technical and thought leadership for Data Engineering and Business Intelligence within TS org.
- Create and implement Data Governance strategy for mitigating privacy and security risks- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
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