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Senior Analytics Engineer

Employer
University of Maryland College Park - College Park, MD
Location
Maryland, United States
Salary
Salary Commensurate with experience
Posted Date
Jun 18, 2024

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Position Type
Administrative, Information Systems
Fields
Education, Technology
Employment Type
Full Time
The Business Intelligence (BI) unit within University Relations at the University of Maryland is seeking a Senior Analytics Engineer with extensive experience in dbt (data build tool) to join our team. This role is crucial in modernizing our data workflows and transforming raw data into valuable insights. This person will have a strong background in data engineering and analytics, with a focus on implementing and optimizing dbt workflows.   Key Responsibilities:
  1. Data Transformation and Modeling: Lead the development and maintenance of analytics workflows using dbt to clean, transform, and document raw data into consumable information and business logic. Ensure high data quality and reliability in the production environment.
  2. Collaboration with Data Teams: Work closely with data analysts, data engineers, and other stakeholders to understand business needs and deliver scalable data solutions. Facilitate the use of modern data warehouses and self-service analytics tools.
  3. Data Quality and Governance: Implement robust data testing frameworks to ensure the accuracy and consistency of data. Document all business logic and maintain up-to-date data models.
  4. Performance Optimization and Support: Optimize SQL queries and data pipelines for performance and cost efficiency. Provide support for ad-hoc data requests and troubleshooting as needed.

 

30/60/90 Day Plan:
  1. First 30 days: Onboard with the team, understand current data infrastructure, and begin familiarizing with existing data pipelines and dbt models.
  2. Next 60 days: Start developing and optimizing dbt models, implement data quality tests, and collaborate with stakeholders on data requirements.
  3. By 90 days: Fully integrate into the team, manage and optimize data pipelines, and deliver the first set of transformed data models for stakeholder review.

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