The Senior Data Engineer will oversee the department's data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysis. This role will work closely and collaboratively with the CIO to define requirements, mine and analyze data, integrate data from a variety of sources, and deploy high-quality data pipelines in support of the Organization.
The ideal candidate is AI curious and has played with LLM's personally or professionally (Kaggle experience works, too). In this role you will help train LLM's with data from the company.
Essential Duties and Responsibilities:
- Maintain and build on our data warehouse and analytics environment
- Design, implement, test, deploy, and maintain stable, secure, and scalable data engineering solutions and pipelines in support of data and analytics projects, including integrating new sources of data into our central data warehouse, and moving data out to applications and affiliates
- Build reports and data visualizations, using data from the data warehouse and other sources
- Produce scalable, replicable code and engineering solutions that help automate repetitive data management tasks
- Perform ad hoc data integration and management, and analysis on a wide variety of data sources and projects
- Implement and monitor best in practice security measures in our OLTP and OLAP databases
- Help other IT staff troubleshoot and tune their SQL, Python, or R code
- Mentor other members of the data team
Skills and Qualifications:
- Demonstrable Proficiency: Candidates will be required to demonstrate proficiency in essential skills during the interview process.
- BS or BA in CS, CIS, MIS or related discipline.
- 7 years’ experience in relational databases and SQL. Extract, Transform, and Load (ETL) data into a relational database.
- 7 years’ experience in data transformation and manipulation.
- 7 years’ experience in delivering reports.
- Proficiency with Python or R preferred but not required.
- Use APIs or SSIS to push and pull data from various data systems and platforms.
- Experience with AI including but not limited to LLM, SLM, ML.
- Experience working with cloud infrastructure services like Amazon Web Services and Microsoft Azure preferred.
- Experience with advanced data visualization and mapping are helpful, but not required.
