Mid-level Cloud Data Architect || 100% Remote role
About The Position
The Cloud Data Architect will work on cloud architecture, data engineering and storage, big data and streaming, AI and machine learning, programming, and solution architecture. The role involves designing and working with cloud-native services and deployment models, with attention to security, scalability, and cost optimization. Data technologies include SQL and NoSQL databases, data warehouses and lakes, and platforms such as Snowflake, Google BigQuery, Amazon Redshift, and Databricks. The position also involves ETL/ELT pipelines, data integration, governance, and modeling. The role requires hands-on work with distributed processing and real-time streaming technologies, including Apache Spark, Apache Kafka, and Apache Flink, as well as AI/ML capabilities such as machine learning, deep learning, generative AI, and large language models. Related areas include model training and fine-tuning, prompt engineering, vector databases, RAG architecture, AI lifecycle management, MLOps/DevOps, CI/CD, model deployment, monitoring, and automation. Programming work includes Python, Java, and Scala, along with API development, backend services, data processing scripts, and AI integration. The architect is expected to understand microservices, APIs, distributed systems, enterprise architecture, and scalable solution design. The role is described as 100% remote and a contract of 6+ months.
Requirements
- Strong background in SQL and NoSQL databases.
- Knowledge of data warehouses, data lakes, ETL/ELT pipelines, data integration, data governance, and data modeling.
- Experience with cloud architecture, deployment models, security, scalability, cost optimization, and cloud-native services.
- Experience with Snowflake, Google BigQuery, Amazon Redshift, or Databricks.
- Hands-on experience with Apache Spark, Apache Kafka, and Apache Flink.
- Knowledge of real-time streaming, distributed processing, large-scale data handling, and event-driven architecture.
- Expertise in machine learning, deep learning, generative AI, and large language models (LLMs).
- Knowledge of model training, fine-tuning, prompt engineering, vector databases, RAG architecture, and AI lifecycle management.
- Experience with MLOps/DevOps, CI/CD pipelines, model deployment, monitoring tools, and automation.
- Strong coding skills in Python; Java and/or Scala are also listed.
- Knowledge of API development, backend services, data processing scripts, and AI integration coding.
- Understanding of microservices, APIs, distributed systems, enterprise architecture, and scalable solution design.
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What This Job Offers
Career Level
Mid Level
Education Level
No Education Listed
