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Build and maintain scalable data pipelines and cloud-native infrastructure to ingest and optimize multi-modal scientific datasets for ML model training in drug discovery.
Build and optimize cloud-native data pipelines and lakehouse infrastructure to process multi-omics datasets for ML-driven drug discovery in a TechBio company.
Build secure, scalable data pipelines and platforms for government clients, transforming sensitive data into trusted assets for analytics and AI using Python, SQL, and cloud tools.
Build and maintain cloud-native MLOps and data pipelines for a biotech firm, deploying and scaling ML models in AWS/GCP while ensuring reliability, security, and reproducibility.
Build and own large-scale distributed data pipelines using Airflow/Dagster, Spark, dbt, Kafka, and AWS to power global retail analytics and decision-making.
Build secure, scalable data pipelines and AI-ready platforms for Defence customers using Python, Kafka, Spark, and Kubernetes in air-gapped environments.
Build and deploy AI/ML models for a public-sector client, focusing on OCR, object detection, and LLM fine-tuning using Python, PyTorch, Hugging Face, and AWS serverless tools.
Working with Us Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens…
Design and lead enterprise-scale data platforms, pipelines, and ML solutions for clients, using cloud-native tools and modern architectures like Lakehouse and Data Mesh.
Robotics Automation Engineer Department: AI Digital Solutions Location: Remote, US Compensation: $104.8K – $131K • Annual Performance Bonus up to 7% Employment Type: FullTime Who We Are Welcome to TELUS Digital — where…
Automation Engineer (Robotics) Department: AI Digital Solutions Location: Remote, US Compensation: $104.8K – $131K • Annual Performance Bonus up to 7% Employment Type: FullTime Who We Are Welcome to TELUS Digital —…
Design and build scalable, cloud-native data pipelines and architectures using AWS, GCP, Azure, Snowflake, or Databricks to power real-time and batch analytics for enterprise AI solutions.
Build and maintain data pipelines, APIs, and analytics infrastructure using Python, SQL, Airflow, DBT, DynamoDB, and AWS Lambda to power healthcare recruitment insights and ML models.
Own and refactor NALA’s data transformation layer (dbt + Snowflake) to build a governed, testable foundation for reporting, AI agents, and real-time pipelines that power cross-border payments.
Департамент ИТ: Наш департамент активно развивается и внедряет цифровые технологии в сфере страхования. Мы одно из ключевых подразделений компании с интересными задачами и современными подходами для их решения. Многие…
Lead data engineer New Zealand (fully remote) · Full-time · Data Engineering · Reports to Head of Data Platforms and Engineering About EVT At EVT we believe in changing the game. Why? Because no one wants ordinary. If…
Build and maintain cloud data pipelines and datasets to track cloud costs, usage, and performance for engineering and finance teams using AWS, Snowflake, Python, and SQL.
Build and own large-scale, distributed data pipelines using Airflow/Dagster, Spark, dbt, and Kafka on AWS to process billions of consumer interactions.
Build and own the data platform for a proptech AI agent: model real-estate entities, define canonical metrics, and enable self-serve analytics for Finance, Growth and Ops using BigQuery, dbt and Python.
Designs and builds scalable data pipelines and automation tools to ingest and process healthcare datasets, primarily using AWS and Snowflake, to power an AI-driven pharma commercial platform.
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