An analytics engineer typically performs the following tasks:

  1. Data Modeling: Analytics engineers create and optimize data models, schemas, and structures to support efficient data storage, retrieval, and analysis within the data platform.
  2. Data Integration: They integrate data from multiple sources, resolving issues such as inconsistencies, duplicates, and format differences to create a unified view of the data.
  3. Data Quality Assurance: Analytics engineers implement processes, tools, and tests to monitor and ensure data quality, completeness, and consistency throughout the data lifecycle.
  4. Data Contracts: While not responsible for defining the data contracts, Analytics Engineers are responsible for enforcing the Data Contracts on any transformed data.
  5. Productionization: They package and deploy data analytics solutions as production-ready, scalable, and maintainable products or services for consumption by data scientists, analysts, or business users.
  6. Documentation and Collaboration: Analytics engineers document data pipelines, models, and processes, and collaborate with data scientists, analysts, and other stakeholders to understand requirements and provide reliable data for analysis.
  7. Performance Optimization: They optimize the performance of data transformations, queries, and analytics processes to ensure efficient and timely data processing and analysis.
  8. Monitoring and Troubleshooting: Analytics engineers monitor data pipelines and analytics processes, identifying and resolving issues or bottlenecks to ensure smooth and reliable operations.

In essence, an analytics engineer acts as a bridge between data engineering and data science, enabling efficient, scalable, and reliable data analytics by applying software engineering principles and best practices.