Data Engineer

Data Engineer gains access to data across the organization and provides ongoing analysis of the data by monitoring, profiling and analyzing databases. Requires a mix of functional, data and technical skills. The right candidate must be able to understand business requirements, translate them into information needs and implement those requirements using data available. The hire will be responsible for expanding and optimizing data architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems. The Data Engineer will support our software developers, database architects, and data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects.

Core job duties include:

1. Assemble large, complex data sets that meet functional / non-functional business requirements.
2. Strong knowledge of SQL required. Ability to identify sets and subsets of information across multiple joins or unions of tables is preferred in addition to writing and troubleshooting SQL queries for data mining
3. Perform complex data analysis and investigation for customer requests to explain results and to make appropriate recommendations.
4. Strong understanding of data modeling concepts
5. Problem solver with the initiative to think critically to identify improvement opportunities (error detection, error correction, root cause analysis)
6. Understand ETL that will aid in verification and testing of data
7. Build processes supporting data transformation, data structures, metadata, dependency and workload management.
8. A successful history of manipulating, processing and extracting value from large disconnected datasets.
9. Analyze business objectives and develop data solutions to meet customer needs.
10. Demonstrated ability to effectively participate in multiple, concurrent projects
11. Improve and customize current data solutions to meet business functional and non-functional requirements.
12. Research new and existing data sources in order to contribute to new development, improve data management processes, and make recommendations for data quality initiatives.
13. Perform periodic data quality reviews for internal and external data.
14. Ensure timely resolution of queries and data issues.
15. Look for new ways to find and collect data by researching potential new sources of information.
16. Work with data and analytics experts to strive for greater functionality in our data systems.