Lead Assistant Manager - Data Engineering

The Lead 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.