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Python Developer (jnr/mid/snr) - both contracting/ICO and full-time employment
You will design, build, and maintain infrastructure for collecting, filtering, transforming, and delivering data
You will be the creator and caretaker of our team’s data pipelines, ensuring a continuous flow of data for analytics
Cooperate closely with our team to align on strategy and the best way on how to streamline the data acquisition and labeling process to make our datasets grow without limit.
You will establish a foothold for the future evolution of the DataOps team as one of its first hires.
About you
You are a Python Engineer who loves data
You have working knowledge of SQL and/or No-SQL databases and have a good idea of how to make them work at scale.
You have experience with managing or building ETL pipelines in any shape or form.
You have experience with workflow management frameworks (e.g. Argo, Airflow, KubeFlow, etc.)
You have experience with K8s, cloud platforms, and event-driven systems.
You understand the importance of data in data-driven and machine learning-based systems.
You love to solve large-scale problems by 3Es: easy to maintain, easy to extend, easy to scale.
You are used to taking end-to-end responsibility for features – from discovery and design to delivery and deployment.
You are honest and bullshit-free. You base your opinions on data, but don’t cling to it in the face of good arguments.
Our tech-stack
Our backend services are written in Django or Flask.
We are gradually chipping away loosely coupled microservices from our core components, picking the cases where it improves scaling and reliability.
From being heavy REST API users, we are moving communication of our internal components towards message queues. We most like connecting our services by RabbitMQ or Apache Pulsar.
We use PostgreSQL for our primary databases, partitioned to ensure queries are fast enough on our table sizes.
Our ML models are built with Keras and Tensorflow, and we like to rely on Kubeflow for most training and experiments.
All our services are deployed in Kubernetes clusters: currently in AWS and Azure.
Our deployments and releases are 98% based on GitOps, with infrastructure defined as code and managed by GitLab-based CI+CD pipelines.