Cloud Data Engineer

CloudAI Technologies is looking for a Cloud Data Engineer to design, build, and maintain modern cloud data platforms and pipelines. The ideal candidate has hands-on experience with Databricks, Snowflake, AWS and/or Azure and has worked on enterprise data lakes, lakehouses, data warehouses, and large-scale data integration solutions.

You will work with our cloud, data, AI, and application engineering teams to build scalable data platforms that support analytics, reporting, AI/ML, and enterprise applications.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and cloud data platforms.
• Build and support data lakes, lakehouses, and cloud data warehouses.
• Develop solutions using Databricks and/or Snowflake.
• Implement data solutions on AWS and Azure.
• Develop batch and, where required, streaming data pipelines.
• Ingest and transform structured, semi-structured, and unstructured data from databases, APIs, SaaS platforms, files, and cloud storage.
• Implement data transformation and processing using SQL, Python and Spark/PySpark.
• Design efficient data models and curated data layers for analytics and downstream applications.
• Implement data quality, validation, monitoring, logging, and error-handling frameworks.
• Optimize pipelines and workloads for performance, reliability, and cloud cost.
• Work with DevOps/platform teams to implement CI/CD and Infrastructure-as-Code practices for data workloads.
• Support data governance, metadata, lineage, security, and access-control requirements.
• Troubleshoot production data issues and participate in root-cause analysis.
• Collaborate with architects, analysts, AI engineers, developers, and client stakeholders.

Required Qualifications

• 5+ years of professional data engineering experience.
• Strong hands-on experience with Databricks and/or Snowflake.
• Experience with AWS and/or Azure data services.
• Practical experience designing or building data lakes or lakehouse architectures.
• Strong SQL skills.
• Proficiency with Python and PySpark/Spark.
• Experience building production-grade ETL/ELT pipelines.
• Understanding of dimensional modeling, data warehousing, and modern data architecture.
• Experience with cloud object storage such as Amazon S3 or Azure Data Lake Storage.
• Familiarity with Git and CI/CD practices.
• Strong troubleshooting and analytical skills.

Preferred Qualifications

Experience with Delta Lake, Unity Catalog, Snowflake Snowpark, AWS Glue, Azure Data Factory, Microsoft Fabric, Kafka/Event Hubs/Kinesis, dbt, Terraform, data governance/catalog platforms, or enterprise AI/ML data pipelines is advantageous.