Assistant Manager

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. 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. 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.