Data Engineer

• Design and implement Amazon Connect flows, routing logic, customer journey orchestration, and decision frameworks using customer, transaction, and operational data.
• Connect multiple data tables and sources such as SAP, CRM, ERP, SQL databases, APIs, event streams, and cloud storage into decision workflows.
• Build scalable ETL/ELT pipelines using Databricks, PySpark, Spark SQL, Delta Lake, and Databricks Workflows for batch and near real-time use cases.
• Develop predictive analytics, segmentation, next-best-action, recommendation, NLP, and GenAI-based insight solutions to support business decisions.
• Use AWS services such as Lambda, S3, Glue, Redshift, Kinesis, SageMaker, Athena, Step Functions, IAM, and CloudWatch for secure and scalable architecture.
• Create dashboards, executive reports, and operational insights using Power BI, Amazon QuickSight, or Tableau; translate data outputs into business recommendations.

What your background should look like

• Minimum 3+ years of professional experience in data engineering, analytics, AI/ML, or cloud data platform engineering. 

• Hands-on experience integrating enterprise data tables and source systems into customer engagement or decisioning platforms. 

• Strong experience with Amazon Connect implementation, integrations, flows, and customer interaction analytics. 

• Strong hands-on experience with Databricks, Delta Lake, PySpark, SQL, and lakehouse data architecture. 

• Experience building AI/ML or advanced analytics solutions that generate actionable business insights. 

• Bachelor's degree in Computer Science, Engineering, Data Science, Information Technology, or a related field.