Cloud Data Engineer

We are looking for a Cloud Data Engineer, who will be responsible for planning, design, developing and maintaining data models, data pipelines and standards for various Data Integration & Data Lake and Data Warehouse projects in the AWS Cloud. Ensure new features and subject areas are modelled to integrate with existing structures and provide a consistent view. Develop and maintain documentation of the data architecture, data flow, and data models of the data warehouse appropriate for various audiences. Adopt Cloud technologies and industry best practices in the field of data lakes and data warehouse architecture and modelling. The role is based in our Gurugram, India location which has a flexible hybrid work model that has employees in-office 2-3 days per week and 2-3 days per week remote. This role requires a broad range of skills and the ability to step into different roles depending on the size and scope of the project. • Minimum 3 years of experience working as a Data Engineer • Must have experience with cloud-based data platforms, data lakes, cloud data warehouse (Redshift or Snowflake). • Proven direct experience with traditional databases, RedShift, AWS Glue, AWS Bedrock, Athena, AWS Lambda, Sagemaker, Lake Formation, IAM, SQS, S3 and other AWS core services. • Expertise in SQL and write optimized SQLs across platforms. • Expertise in writing python scripts and ability to manipulate data using Pandas/Pyspark. Familiarity and usage of different file formats in batch processing like Parquet/ORC and Open table formats. • Experience integrating data from CRM, ERP, and marketing platforms (Salesforce, Eloqua, Oracle) into data lake/warehouse. • Experience in data modelling, ELT, implementing complex stored Procedures and standard DWH and ETL/ELT concepts. • Experience in advanced concepts like setting up resource monitors, Role-based access controls (RBAC), query performance tuning, time travel and understanding how to use these features. • Experience in deploying features such as data sharing, events, and lake-house patterns. • Provide resolution to an extensive range of complicated data pipeline related problems, proactively and as issues surface. • Experience with data security and data access controls and design. • Strong analytical, troubleshooting, and critical thinking skills. • Familiarity with Devops tools for version control, CI/CD, GitHub. • Exposure to Enterprise BI tools like Power BI and ThoughtSpot. • Experience developing and supporting production data pipelines. • Exposure to AI and leveraging AI tools for daily tasks. • Effective communication skills with ability to translate complex technical work to business users.