About this role
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL’s Human Resources team, as well as our hiring managers.
Core Technologies
• DBT (Data Build Tool) • Google BigQuery • SQL • Python • PySpark • Git
GCP Services
• BigQuery • Cloud Storage • Dataproc • Cloud Composer (Airflow) • Dataflow • IAM
SAS Migration & Modernization
• Analyze existing SAS datasets, ETL jobs, PROC SQL code, and SAS macros. • Convert SAS transformation logic into DBT models and cloud-native data pipelines. • Support migration of historical and incremental data from SAS platforms to GCP. • Participate in migration assessments, code conversion, and reconciliation activities.
Data Engineering & Development
• Design and develop scalable ELT pipelines using DBT and BigQuery. • Build and optimize data ingestion, transformation, and aggregation processes. • Develop reusable and modular DBT models following best practices. • Implement performance tuning for SQL and BigQuery workloads. • Create and maintain data lineage and metadata documentation.
Data Quality & Reconciliation
• Design and implement data quality validation frameworks. • Perform source-to-target reconciliation between SAS and GCP platforms. • Investigate and resolve data inconsistencies and migration issues. • Support automated testing and monitoring of data pipelines.
Graduate in Computer Science, Data Science, or related field. 6-8 years of experience in data engineering or related field.