Design and implement data pipelines to ingest, transform, and load data into data warehouses and data lakes.

Develop and maintain data models and schemas that support business requirements and ensure data quality.

Collaborate with data scientists and business analysts to understand data needs and provide data solutions.

Optimize data infrastructure for performance, scalability, and reliability.

Implement data governance policies and procedures to ensure data security and compliance.

Monitor data pipelines and data quality, and troubleshoot data-related issues.

Automate data processes and workflows to improve efficiency and reduce manual effort.

Create and maintain documentation for data pipelines, data models, and data processes.

Stay up-to-date with the latest data engineering technologies and trends.

Participate in code reviews and contribute to the development of data engineering best practices.

Architect robust data pipelines for efficient data ingestion, transformation, and loading.

Develop and maintain ETL processes using tools like Apache Spark, Airflow, or Informatica.

Create and optimize data models for analytical databases and data warehouses.

Write and optimize complex SQL queries for performance and scalability.

Utilize cloud-based data services such as AWS, Azure, or GCP for data storage and processing.

Implement data quality checks and monitoring to ensure data accuracy and reliability.

Identify and resolve performance bottlenecks in data pipelines and analytical systems.

Work closely with data scientists, analysts, and business stakeholders to understand data requirements.

Create and maintain documentation for data pipelines, data models, and ETL processes.

Implement data security measures to protect sensitive data and ensure compliance.