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.