Liaising with coworkers and clients to elucidate the requirements for each task.

Conceptualizing and generating infrastructure that allows big data to be accessed and analyzed.

Reformulating existing frameworks to optimize their functioning.

Testing such structures to ensure that they are fit for use.

Building a data pipeline from different data sources using different data types like API, CSV, JSON, etc.

Preparing raw data for manipulation by Data Scientists.

Implementing proper data validation and data reconciliation methodologies.

Ensuring that your work remains backed up and readily accessible to relevant coworkers.

Remaining up-to-date with industry standards and technological advancements that will improve the quality of your outputs.