Architect robust, scalable data pipelines using tools like Apache Kafka, Apache Spark, and cloud-based services (AWS, Azure, GCP).

Develop and maintain ETL (Extract, Transform, Load) processes to ingest data from various sources into data warehouses or data lakes.

Design and implement data models and schemas optimized for performance, scalability, and analytical needs.

Manage and optimize data warehouse solutions (e.g., Snowflake, Amazon Redshift) for efficient data storage and retrieval.

Process large datasets using distributed computing frameworks like Hadoop and Spark to extract valuable insights.

Implement data quality checks and monitoring systems to ensure data accuracy, completeness, and consistency.

Optimize data pipelines and queries for performance, scalability, and cost-effectiveness.

Manage and maintain cloud-based data infrastructure, including storage, compute, and networking resources.

Implement data security measures to protect sensitive data from unauthorized access and ensure compliance with data privacy regulations.

Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver data solutions.

Document data pipelines, data models, and data engineering processes to facilitate knowledge sharing and collaboration.