We are seeking an experienced Cloud Data Engineer with a robust background in AWS, Azure, and GCP. The ideal candidate will have extensive experience with cloud-native ETL tools such as AWS DMS, AWS Glue, Kafka, Azure Data Factory, GCP Dataflow, and other ETL tools like Informatica, SAP Data Intelligence, etc. You will be responsible for designing, implementing, and maintaining robust data pipelines and building scalable data lakes. Experience with various data platforms like Redshift, Snowflake, Databricks, Synapse, Snowflake and others is essential. Familiarity with data extraction from SAP or ERP systems is a plus.

Key Responsibilities:

Design and Development:
- Design, develop, and maintain scalable ETL pipelines using cloud-native tools (AWS DMS, AWS Glue, Kafka, Azure Data Factory, GCP Dataflow, etc.).
- Architect and implement data lakes and data warehouses on cloud platforms (AWS, Azure, GCP).
- Develop and optimize data ingestion, transformation, and loading processes using Databricks, Snowflake, Redshift, BigQuery and Azure Synapse.




- Implement ETL processes using tools like Informatica, SAP Data Intelligence, and others.
- Develop and optimize data processing jobs using Spark Scala.

Data Integration and Management:
- Integrate various data sources, including relational databases, APIs, unstructured data, and ERP systems into the data lake.
- Ensure data quality and integrity through rigorous testing and validation.
- Perform data extraction from SAP or ERP systems when necessary.

Performance Optimization:
- Monitor and optimize the performance of data pipelines and ETL processes.
- Implement best practices for data management, including data governance, security, and compliance.

Collaboration and Communication:
- Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
- Collaborate with cross-functional teams to design and implement data solutions that meet business needs.

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📌 Cloud Data Engineer (India)
🏢 Bright Tier Solutions
📍 India