The candidate will be responsible for meeting with business and technical staff, end users, and senior management to define requirements. The candidate must also be able to develop, deploy, and support Data Engineering. The ideal candidate will possess effective communication and interpersonal skills to build and maintain working relationships with clients. The developer will also be expected to prepare and maintain related technical documentation.
- Work with clients to elicit, refine, and document requirements.
- Data modeling, process modeling, and rapid prototyping.
- Develop/maintain ETL packages.
- Develop/maintain Power BI data models and visualizations.
- Assist in the design and implementation of a data lake to store structured and unstructured data.
- Plan, prioritize, and execute in a rapidly changing, fast-paced environment.
- Use version management and issue tracking software to document all changes.
- Conduct tuning in Power BI to improve performance.
- Bachelors’ Degree in computer science, engineering, mathematics, statistics, data science, analytics bioinformatics, or related program.
- 3-5 years proven experience with Power BI and/or SSAS/SSIS.
- Experience with TSQL.
- Application architecture experience.
- Excellent interpersonal and organizational skills.
- Strong leadership, verbal and written communication skills.
- U.S. Citizenship Required
- Project management understanding.
- Consulting experience a plus.
- Ability to obtain a security clearance
- Experience with Power Query/M functions.
- ETL experience and/or ML experience with Apache Spark or Apache Hadoop
- Azure Data Engineer, Azure Data Scientist, Azure Data Analyst Associate or MCSA: Machine Learning Certification
- 1+ years of experience with data science, econometrics, statistics, machine learning, or analytics in professional or academic environments
- 1+ years of experience with managing and manipulating large data sets, developing data science approaches, and executing data science tasks
- Experience with machine learning models and applications
- Ability to leverage a wide variety of data science capabilities and languages
- Ability to communicate results effectively to both technical and nontechnical audiences