I have led a data engineering team of 10 members, overseeing the end-to-end data pipeline from extraction to visualization. My role involved setting project goals, mentoring team members, and ensuring the team adhered to best practices in data management. I also facilitated cross-functional collaboration to align data initiatives with business objectives.

I implement a robust data governance framework that includes regular data audits, automated validation checks, and clear data ownership roles. By leveraging tools like Apache Airflow and data quality libraries, I ensure that data pipelines are monitored for anomalies and that any issues are promptly addressed.

I focus on modularizing the pipeline architecture to enhance scalability and maintainability. By using cloud-based solutions like AWS or GCP, I ensure cost-effective storage and processing. Additionally, I implement CI/CD practices to automate testing and deployment, reducing the risk of errors and improving pipeline efficiency.

I prioritize data security by implementing encryption at rest and in transit, access controls, and regular security audits. Compliance with regulations like GDPR or HIPAA is ensured through strict data handling policies and regular training for the team. I also use tools that provide visibility into data access and usage.

One challenging project involved integrating data from multiple legacy systems into a unified data warehouse. The main obstacle was the inconsistency in data formats and quality. I overcame this by creating a data reconciliation process and using ETL tools to standardize the data. Additionally, I engaged with stakeholders to understand their data needs and adjusted the pipeline accordingly.