Position: Data Engineer
Company: MoveUp
Category: Data Engineer
Experience required: 3y
English level: intermediate

We are the virtual assistant for restaurants.
Its first skill is already being deployed at scale: automated order taking in Quick Service Restaurants (QSRs) where it improves customer experience, increases sales, and reduces employee stress.

A thorough understanding of drive-through voice orders requires deep technical knowledge. To tackle this big challenge, we're building our own Speech Recognition and Natural Language Understanding stack from the ground up. 

Are you up for the challenge ?

We have complex pipelines generating the data for all our ML algorithms and the production environment. We are looking for a sharp Data engineer who is enthusiastic about managing and analyzing big data flows in different environments.
This person will take full responsibility over the full data flow. Make sure that the data fits our needs, create processes to monitor and validate the data flow, create tools to improve the interaction of different teams with the data and will always be looking to improve the work processes.

You would bring:
- 4+ years experience in Python and Database.
- Experience in one of the cloud environments (AWS, GCP, Azure).
- Qualities: Star performer, Team player, Fast, Strong ownership, Passionate about their work, Great communicator. 
 
Bonus: 
- 4+ years of python experience
- Experience with data pipelines, MLOps.
- Experience in C/C++.
- MSc in relevant domain.

In This Role You Will:
- Be responsible for the full data creation pipeline.
- Analyze the data aggregated at different points in the pipeline and create automated tools to detect and fix problems.
- Work hand in hand with the ML engineers and analyze the ML algorithms results to detect faults / improvement suggestions in the data creation and devise the strategies for solution.
- Understand the operation of the different teams interacting with the data and will create tools and processes to ease and improve their processes.
- Analyze the data flow and performance in the actual production environment.
- Create a centralized DB tracking and managing all facets of the data.