EXL Talent Acquisition Team-posted 3 months ago
5,001-10,000 employees

The position involves performing SAS Data Quality (DQ) conversion tasks to modernize and optimize legacy data processes. The role requires writing, optimizing, and translating complex SQL queries across multiple SQL dialects, with a focus on Teradata and SAS SQL. Collaboration with data architects, analysts, and business stakeholders is essential to understand data requirements and deliver reliable data pipelines. Additionally, the role includes assisting in data integration, transformation, and migration efforts across platforms, monitoring and troubleshooting ETL jobs and data workflows to ensure data accuracy and reliability, participating in code reviews, testing, and documentation to maintain high-quality standards, and supporting data governance and quality initiatives as part of the data engineering team.

  • Perform SAS Data Quality (DQ) conversion tasks to modernize and optimize legacy data processes.
  • Write, optimize, and translate complex SQL queries across multiple SQL dialects, with an emphasis on Teradata and SAS SQL.
  • Collaborate with data architects, analysts, and business stakeholders to understand data requirements and deliver reliable data pipelines.
  • Assist in data integration, transformation, and migration efforts across platforms.
  • Monitor and troubleshoot ETL jobs and data workflows to ensure data accuracy and reliability.
  • Participate in code reviews, testing, and documentation to maintain high-quality standards.
  • Support data governance and quality initiatives as part of the data engineering team.
  • Proficient in SQL with practical experience translating between two or more SQL dialects.
  • Experience with SAS Data Quality (DQ) tools and/or SAS data processing.
  • Working knowledge of Teradata SQL dialect preferred.
  • Understanding of ETL concepts and data pipeline development.
  • Strong problem-solving skills and attention to detail.
  • Ability to work collaboratively in a team and communicate effectively with technical and non-technical stakeholders.
  • Prior experience in data warehousing and large-scale data environments.
  • Familiarity with other SQL dialects such as PostgreSQL, MySQL, or Microsoft SQL Server.
  • Basic knowledge of Python or other scripting languages used for data engineering.
  • Exposure to cloud platforms (AWS) and cloud-based data services.
  • Understanding of data governance, data quality frameworks, and best practices.
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