Python Developer (jnr/mid/snr) - both contracting/ICO and full-time employment

What the job is about

• 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.