Job Summary :

We are seeking an experienced Senior Developer to design and optimize data solutions using PL/SQL, SQL, ETL, and Microsoft SSIS, supporting enterprise data warehousing initiatives in a hybrid work model.

This role centers on building robust scheduling workflows and integrating multi-channel and supply chain data to improve operational insights and drive informed business decision-making for global stakeholders.

Key Responsibilities :

- Design and develop complex PL/SQL and SQL procedures, functions, and queries that ensure accurate, performant, and reliable data processing for enterprise reporting needs.

- Implement and optimize ETL workflows using Microsoft SSIS to move, transform, and validate large datasets from diverse operational systems into centralized warehouse structures.

- Configure and manage scheduling for batch jobs and data pipelines to guarantee timely data availability aligned with business-day operations and service level expectations.

- Analyze existing data warehousing concepts and models to propose improvements that enhance scalability, maintainability, and analytical value across multiple business units.

- Collaborate with cross-functional teams to translate multi-channel and supply chain management data requirements into well-structured technical solutions supporting strategic initiatives.

- Perform detailed root cause analysis and remediation of data quality issues to ensure downstream analytics, dashboards, and operational decisions are based on trustworthy information.

- Document technical designs, mappings, data flows, and deployment steps in clear, reusable formats to support ongoing maintenance, future enhancements, and audit readiness.

- Conduct unit, integration, and regression testing of PL/SQL, SQL, and ETL components to verify functional accuracy, performance benchmarks, and alignment with enterprise standards.

- Provide production support for data warehouse jobs and SSIS packages by monitoring execution, troubleshooting failures, and implementing preventive improvements to reduce recurrence.

- Engage with stakeholders in a hybrid work setting to gather requirements, demonstrate solutions, and refine deliverables to consistently meet business expectations.

- Contribute to continuous improvement of data engineering practices by proposing new patterns, tools, and methods that increase automation, reliability, and transparency of data flows.

- Align daily development activities with organizational goals, ensuring data solutions enable better customer experiences, optimized supply chains, and informed decision-making across markets.

- Ensure compliance with internal quality guidelines and external regulatory expectations by implementing robust controls, validations, and traceability within data warehouse ecosystems.