EXL—Lead Assistant Manager

Job description

Description: We are looking for a skilled Data Engineer with strong DBT experience to design, develop, and optimize scalable data transformation pipelines on modern cloud data platforms. The ideal candidate should have hands-on expertise in DBT (Data Build Tool), SQL, cloud data warehouses, and ELT processes. The role will involve building analytics-ready datasets, implementing data quality frameworks, and collaborating with data architects, analysts, and business stakeholders to deliver reliable and scalable data products. Requirements: Required Skills Core Technical Skills - DBT (Data Build Tool) - Advanced SQL - Data Warehousing Concepts - Data Modeling - ETL / ELT Development Cloud Platforms - Snowflake - Databricks - Google BigQuery - Azure Synapse Analytics - AWS Redshift Programming - Python - SQL - Shell Scripting (Preferred) Data Engineering Tools - Airflow - Azure Data Factory - Databricks Workflows - GitHub / GitLab Data Quality & Governance - Data Lineage - Data Catalog - Data Validation Frameworks - Metadata Management Desired Experience - Experience building DBT models on Snowflake or BigQuery. - Experience implementing data validation checks and automated testing through DBT. - Experience creating analytics-ready datasets and semantic models. - Exposure to Medallion Architecture, Lakehouse, and Modern Data Platforms. Qualifications - Bachelor's degree in Computer Science, Engineering, Information Technology, or related field. - 4–8 years of Data Engineering experience. - Minimum 2+ years of hands-on DBT development experience. Preferred Certifications - SnowPro Certification - Databricks Data Engineer Associate/Professional - Google Professional Data Engineer - Microsoft Azure Data Engineer Associate Nice-to-Have Skills - DBT Cloud - Jinja Macros - Terraform - Kafka - Spark/PySpark - Data Vault 2.0 AI-assisted development tools (GitHub Copilot, Microsoft Copilot) Responsibilities: Key Responsibilities Data Engineering & ELT Development - Design and develop ELT pipelines using DBT. - Build reusable, scalable, and maintainable data transformation models. - Develop staging, intermediate, and mart layers following DBT best practices. - Implement modular SQL transformations and macros. - Create and maintain source-to-target mappings. Data Modeling - Design dimensional models, fact tables, and star schemas. - Build business-friendly semantic layers for reporting and analytics. - Support enterprise data warehouse initiatives. Data Quality & Testing - Implement DBT tests for: - Uniqueness - Referential integrity - Null validation - Custom business rules - Monitor and improve overall data quality. - Perform root cause analysis for data issues. Cloud Data Platform Development - Work with cloud platforms such as: - Snowflake - Databricks - BigQuery - Azure Synapse - Redshift - Optimize query performance and manage compute costs. CI/CD & DevOps - Integrate DBT projects with Git and CI/CD pipelines. - Automate deployments across Development, QA, and Production environments. - Maintain documentation and lineage using DBT documentation features. Collaboration - Work closely with Data Architects, Analysts, and Business Teams. - Participate in Agile ceremonies and sprint planning. - Translate business requirements into scalable data solutions.