Data Engineer/Architect EDGE|ML

Data Engineer/Architect EDGE|ML Description EDGE|ML is an end-to-end analytics and solutions platform aiming to supply SCM, logistics, store and category managers with data, insights, and solutions to optimize their processes. EDGE|ML is built on a state-of-the-art, scalable technology leveraging Google Cloud Platform managed services. The ideal candidate for the Data engineer/architect role is an experienced data pipeline/architecture builder who enjoys building from the ground up and optimizing data systems. Your challenges Augment EDGE|ML offerings by enhancing, extending, optimizing, and operationalizing the analytic services. Data Architecture Optimally design and work with large, complex datasets in BigQuery. Data Engineering Design, build, maintain, productionize, and operate data pipelines in GCP using Python, SQL, and Google managed services. Deploy machine learning models. Collaborate closely with business stakeholders, data analysts/scientists, software engineers, and platform operations staff. You should bring in Data architecture 5+ years of experience in Data Warehousing, Business Intelligence, Big Data. Advanced working SQL knowledge and experience (CTE, window functions) working with relational databases, as well as working familiarity with a variety of databases. Extensive knowledge of data modelling approaches and best practices. Experience in implementation of a data mesh architecture in Google Cloud is a strong plus. Extensive knowledge of techniques for query optimization in BigQuery. Data Engineering 5+ years of experience in a Data Engineering role Extensive experience building, optimizing, and productionizing big data data pipelines, in Google Cloud Platform. Experience designing and deploying high performance systems with reliable monitoring and logging practices. Experience with source control systems (GitHub) and continuous integration & deployment tools e.g. Jenkins Experience using application containerization technologies (Docker, Kubernetes), orchestration and scheduling (e.g. Google Composer, Apache Airflow) Experience with machine learning using packages such as xgboost, sklearn, TensorFlow (building, deploying, monitoring) Experience building data pipelines for Gen AI product Knowledge of professional software engineering practices, craftsmanship of clean and efficient code Intense curiosity and tenacity in staying ahead of with technology and continually striving to be better at your craft. Strong interpersonal skills, comfortable in communicating with users, other technical teams, and business stakeholders to understand requirements, describe data modeling and data engineering decisions. Ability to contribute to an open, international team culture to drive the knowledge exchange and learning of all team members. Fluency in English (both written and spoken) is a must-have. .