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Python developer - ML-based data solutions in the cloud

· Hiring

We are looking for a Python developer with approximately 0.5 - 3 years of experience in software engineering who is interested in working on building complex data pipelines combining data ingestion, transformations, machine learning and streaming in the cloud (AWS/Azure/GCP) using mainly a data lake style approach and technologies around Spark and Python (Databricks, Airflow, Keboola, ...). We call this role a "data engineer" and it is about working closely with data scientists on the team towards a production-ready version of machine-learning based data solutions. You do not need to be a data pipeline expert - we will help you with that; it's more about having good software engineering practices (CI, testing, versioning, ...) and an interest in the data/AI/ML area.

Who are we?

We are a machine learning and cloud data engineering boutique based in Prague - we help clients across Europe from different industries (finance, retail, e-commerce, HR, etc.) use advanced data analytics (predictive modelling, machine learning, NLP, etc.) to improve their processes such as:

  • social network influencer recommendations

  • predictive cross-sell campaign targeting

  • display ad micro-targeting

  • finding the most relevant candidate for the job with NLP
  • counting merchandise in a store shelf using computer vision, etc.

A group of more than twenty data scientists and data engineers who were bored by large and slow projects in corporations or were lonely in start-ups and set off on another route. We do not have our own product, but we work on a project basis and try to help our clients find the best solutions.

We work in agile / prototype mode and build on modern data and BI technologies, especially in the open-source and cloud world:

  • Data Preparation / ETL - especially Spark (Databricks), Python, SQL, Keboola
  • Machine learning - Spark (Databricks), Python, R
  • Visualization - Qlik Sense, Tableau, PowerBI, Looker
  • Infrastructure - Azure, AWS, GCP

What would you do?

Working on individual client projects typically in tandem with a senior data architect/team lead who gives the project direction and a data scientist – this group of 3 is what we call a data strike team.

As part of such a strike team, you would be responsible for:

  • Building the cloud data platform for the given project in the a cloud environment which we setup for a client using our existing frameworks for AWS/Azure/GCP
  • Developing the data pipelines in some combination of Spark+Python+SQL with focus on good software engineering practice around continuous integration, versioning, testing, etc. - this will typical involve connecting to different external and internal APIs to ingest data, figuring out how to best clean/transform/enrich the data for the given purpose/predictive model and how to automatically ensure machine learning model training and results serving
  • Contributing to the development of our cross-project frameworks for efficiently managing complex data pipelines - topics like orchestration, monitoring and alerting, machine learning model serving, etc.
  • Exploring and experimenting with new data technologies (streaming, distributed systems, unstructured data) and testing how they would fit into the things we do

It's not about how many of these things you already know now, but that you are interested and want to learn them all.

This role could be very interesting for you if:

  • You like working in a smaller cross-functional team on something that has a clear business purpose and use
  • You are interested into getting deeper in data and machine-learning technologies
  • You do not want to explore just one area of data analysis but you are interested in getting to know more clients / industries / projects and technologies

  • You want to work for a smaller flexible company (a combination of a remote and an office, a flat structure, etc.) and to be part of a team of people like you, so you can help and enrich each other

Write to to get to know each other and discuss it in more detail.

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