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Data Engineer.

Remote:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

Poland

Offer summary

Qualifications:

B.SC or Master's in Computer Science, Math, or Engineering, At least 2 years of Python experience, 2 years of hands-on data engineering experience, Familiar with ML-Ops and AI domain, Basic knowledge of Docker/Kubernetes.

Key responsabilities:

Building and maintaining data pipelines
Ensuring accuracy and efficiency of data for model training
Working with structured and unstructured data
Leveraging expertise in data engineering and machine learning
Creating a robust and scalable system

Fetcherr Scaleup https://fetcherr.io/

11 - 50 Employees

See more Fetcherr offers

Job description

Data Engineer

Fetcherr experts in deep learning, algo-trading, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.

We are seeking Data Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

Requirements:

You’ll be a great fit if…

B.SC or Master's degree in Computer Science / Math / Engineering is preferable
At least 2 years of commercial experience in Python – must
At least 2 years hands-on data engineering commercial experience - must
Familiar with ML-Ops and AI domain
Experience working with pipelines orchestrators, preferably Dagster or Airflow
Good understanding of Data Structures and Algorithms
Basic knowledge of Docker and Kubernetes or other, scalable, containerized solutions
Team player, ready to help others
Pro-active with the tasks given to you, often suggesting different / better ideas
Commercial experience working with Dask (preferred) or other distributed computing systems - must
Commercial experience in writing and maintaining scalable systems
English – fluent, both written and spoken