Financial Systems Professional

Job Summary

We are seeking a skilled AWS Data Engineer to join our team, specializing in building, and maintaining modern data pipelines using ETL/ELT methodologies. The ideal candidate will have extensive experience in transforming unstructured data into structured datasets, creating CI/CD automation, and ensuring high-quality, validated data for analytical purposes.

Key Responsibilities

• Data Pipeline Development: Design, develop, and maintain robust ELT pipelines using databricks.
• Data Modeling Transformation: Apply advanced SQL techniques (CTEs, Window functions, joins, aggregations) to build clean, actionable, and scalable data models.
• Automation CI/CD: Develop and maintain automated CI/CD deployment pipelines for dbt projects using Git to ensure smooth production deployments.
• Data Quality Validation: Perform comprehensive validation between source and target tables, utilizing Q-Test for test case creation, execution, and defect tracking.
• Automation: Python scripts for validation and automation.
• Version Control: Git, Pull requests, Code reviews.
• Data Migration: Hands-on experience with migrating data between heterogeneous systems (Excel, Flat Files, XML, JSON) and SQL servers.
• Analytical Skills: Strong problem-solving skills with the ability to adapt quickly in fast-paced environments.
• Production Support: Troubleshoot and resolve production issues, data discrepancies, and job failures to ensure data reliability.
• Stakeholder Collaboration: Coordinate with business users and stakeholders to gather requirements and translate them into technical solutions.
• Documentation: Create technical documentation, demo documents, and wiki pages for all developed jobs and queries.

Preferred Qualifications

• Cloud Platform: AWS
• Databricks
• Oracle PL/SQL (Nice to have)
• Oracle EBS AR (Nice to Have)
• Develop and deploy Agentic AI solutions, including autonomous workflows, AI agents, and GenAI-powered automation tools.