EXL—Assistant Manager

Job description

Description: The Assistant Manager - Data Engineering will be responsible for developing and maintaining data integration solutions, data pipelines, and cloud-based data platforms. The role requires strong expertise in data warehousing, ETL/ELT processes, SQL, Python, and cloud technologies. The individual will collaborate with business stakeholders, analysts, and data science teams to deliver robust data solutions that enable data-driven decision-making. This position requires a blend of hands-on technical expertise, problem-solving skills, and the ability to lead projects while ensuring adherence to data governance, security, and quality standards. Requirements: - Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field. - 5-8 years of experience in Data Engineering, Data Warehousing, or related domains. - Strong proficiency in SQL and Python for data processing and automation. - Experience with cloud platforms such as Azure, AWS, or GCP. - Hands-on experience with data integration and orchestration tools such as Azure Data Factory, Airflow, Informatica, or similar platforms. - Knowledge of modern data platforms including Databricks, Snowflake, Redshift, BigQuery, or equivalent technologies. - Experience with Spark/PySpark and large-scale data processing frameworks. - Strong understanding of data modeling, ETL/ELT concepts, and database optimization techniques. - Familiarity with DevOps, CI/CD practices, and version control systems. - Excellent analytical, problem-solving, stakeholder management, and communication skills. - Experience leading projects or mentoring team members will be preferred. Responsibilities: - Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data. - Build and optimize data models, data warehouses, data lakes, and modern data architectures. - Develop data ingestion, transformation, and integration processes using cloud-native technologies. - Ensure data quality, integrity, reliability, and governance across enterprise data assets. - Collaborate with business, analytics, and data science teams to understand and deliver data requirements. - Implement data pipeline monitoring, troubleshooting, and performance optimization procedures. - Support real-time and batch data processing requirements. - Automate data workflows and deployment processes using CI/CD best practices. - Ensure compliance with security, privacy, and organizational data standards. - Mentor junior team members and provide technical guidance on data engineering best practices. - Participate in architecture discussions and recommend enhancements to improve scalability and efficiency.