Assistant Manager
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Key skills for this role
About the Role
We are seeking an experienced Assistant Manager - Data Engineering to design, build, and optimize scalable data pipelines and modern data platforms. The role will support enterprise analytics, reporting, and AI/ML initiatives by ensuring reliable, secure, and high-quality data availability across the organization.
Key Skills for This Role
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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.
- - 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.
Qualifications
- 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.
- - 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.
About EXL
EXL is a global data and AI company that provides analytics, digital operations, and industry-specific services to enterprises in insurance, healthcare, banking, retail, media, and energy.
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