Architecting robust and scalable data pipelines for efficient data ingestion, processing, and storage.

Developing and maintaining ETL (Extract, Transform, Load) processes to move and transform data.

Managing and optimizing databases, including performance tuning and schema design.

Creating conceptual, logical, and physical data models to support data storage and retrieval.

Utilizing cloud-based data services (e.g., AWS, Azure, GCP) for data storage, processing, and analytics.

Designing and implementing data warehousing solutions for business intelligence and reporting.

Processing large datasets using big data technologies like Hadoop, Spark, and Kafka.

Ensuring data quality through validation, cleansing, and monitoring processes.

Implementing data security measures to protect sensitive information and comply with regulations.

Optimizing data systems for performance, scalability, and reliability.

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

Creating and maintaining documentation for data pipelines, systems, and processes.

Design and develop data pipelines to ingest, transform, and load data from various sources into data warehouses or data lakes.

Build and maintain scalable and reliable data infrastructure, including databases, data warehouses, and data lakes.

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

Collaborate with data scientists and analysts to understand their data needs and provide them with the necessary data access and tools.

Optimize data storage and processing to improve performance and reduce costs.

Automate data integration and ETL processes to streamline data workflows.

Develop and maintain documentation for data pipelines, data models, and data infrastructure.

Troubleshoot and resolve data-related issues, including data quality problems and pipeline failures.

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

Stay up-to-date with the latest data engineering technologies and trends, and evaluate new tools and techniques.

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

Work with cloud platforms (e.g., AWS, Azure, GCP) to build and deploy data solutions.

Use big data technologies (e.g., Hadoop, Spark, Kafka) to process and analyze large datasets.

Create and maintain data catalogs and metadata management systems.

Design and implement data governance policies and procedures.