Education: Bachelor's degree
Experience: 5+ years
We are looking for a skilled Data Engineer for Cloud Data Lake activities. The candidate should have industry experience (preferably in Financial Services) in supporting enterprise applications and exposure to Cloud based Data engineering platforms with deep understanding of data processing frameworks and solid experience with ETL development using distributed computing frameworks as Apache Spark, Scala, Hadoop, Hive.
This hands-on role will partner with senior-level development managers, architects and business leadership to develop and execute the technology product roadmap.
- Collaborate with data scientists, analysts, and product teams to enable analytics and ML use cases - Ensure data quality, reliability, security, and governance across data platforms - Optimize performance and cost of data workloads in AWS and Databricks - Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, CloudFormation) - Monitor, troubleshoot, and resolve data pipeline and platform issues - Document data architecture, pipelines, and operational processes - End to end Cloud Data Lake design & development including data ingestion, data modeling and data distribution. - Build data integrations, hand-offs between on-prem/cloud-based systems. - Design, build, and maintain scalable, reliable data pipelines using AWS services and Databricks - Develop and optimize ETL/ELT workflows for batch and near-real-time data processing - Implement data solutions using Apache Spark (PySpark/Scala) on Databricks - Leverage AWS services such as S3, Glue, Lambda, EMR, Redshift, Athena, and Kinesis -
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