Develop and maintain technical documentation related to SAS data architecture, ETL workflows, and integration processes to ensure clarity and accessibility for all stakeholders. Professional & Technical Skills:
Collaborate with and mentor team members to ensure high performance and adherence to best practices.
Take responsibility for technical decisions within the team related to SAS DI workflows, standards, and solutions.
Conduct knowledge-sharing sessions to elevate team skills and promote continuous learning of SAS tools and data engineering concepts.
Engage with cross-functional teams including data scientists, analysts, business users, and architects to gather data requirements and deliver high-quality, scalable data integration solutions.
Provide solutions to complex data processing problems for both the immediate team and extended project teams.
Requirements
sas distudio
sas etl
sql
data modeling
job scheduling
data governance
Must Have Skills: Strong proficiency in SAS Data Integration Studio (DI Studio), SAS Base, SAS Macro, and SAS Management Console with solid troubleshooting capabilities.
Strong capability to handle and process large datasets efficiently in day-to-day tasks.
Extensive experience in ETL design, development, scheduling, and optimization using SAS tools.
Experience working in enterprise data warehousing environments and familiarity with cloud or on-prem data platforms.
Experience with job scheduling tools (e.g., LSF, Autosys, Control-M) is a plus." . Additional Information: - The candidate should have minimum 5 years of experience in SAS Data Integration. - This position is based at our Bengaluru office. - A 15 years full time education is required.
Working knowledge of SQL and integration with databases such as Oracle, SQL Server, Teradata, or similar.
Expected to act as an SAS Data Integration SME within the team.
Hands-on experience with data pipelines, ETL processes, data cleansing, and transformation workflows.
Strong understanding of data modeling, including dimensional modeling and relational structures.
Ability to design and implement data governance, metadata management, and data quality frameworks.