Job
Description
We are seeking skilled and dynamic Cloud Data Engineers specializing in AWS, Azure, Databricks, and GCP. The ideal candidate will have a strong background in data engineering, with a focus on data ingestion, transformation, and warehousing. They should also possess excellent knowledge of PySpark or Spark, and a proven ability to optimize performance in Spark job executions. Key Responsibilities Design, build, and maintain scalable data pipelines for a variety of cloud platforms including AWS, Azure, Databricks, and GCP. Implement data ingestion and transformation processes to facilitate efficient data warehousing. Utilize cloud services to enhance data processing capabilities AWS Glue, Athena, Lambda, Redshift, Step Functions, DynamoDB, SNS. Azure Data Factory, Synapse Analytics, Functions, Cosmos DB, Event Grid, Logic Apps, Service Bus. GCP Dataflow, BigQuery, DataProc, Cloud Functions, Bigtable, Pub/Sub, Data Fusion. Optimize Spark job performance to ensure high efficiency and reliability. Stay proactive in learning and implementing new technologies to improve data processing frameworks. Collaborate with crossfunctional teams to deliver robust data solutions. Work on Spark Streaming for realtime data processing as necessary. Qualifications 710 years of experience in data engineering with a strong focus on cloud environments. Proficiency in PySpark or Spark is mandatory. Proven experience with data ingestion, transformation, and data warehousing. Indepth knowledge and handson experience with cloud services(AWS/Azure/GCP) Demonstrated ability in performance optimization of Spark jobs. Strong problemsolving skills and the ability to work independently as well as in a team. Cloud Certification (AWS, Azure, or GCP) is a plus. Familiarity with Spark Streaming is a bonus Preferred Skills Python, Pyspark, SQL with (AWS or Azure or GCP) Mandatory skill sets Python, Pyspark, SQL with (AWS or Azure or GCP)
