Data Analytics Engineer

Data Pipeline Development: Design, build, and maintain scalable ETL (Extract, Transform, Load) pipelines to ingest and process large volumes of data from various sources.

Data Warehousing: Develop and manage data warehouses and data marts to store structured and unstructured data, ensuring data integrity, security, and accessibility.

Data Modeling: Create and optimize data models to support efficient querying and reporting.

Data Integration: Integrate data from different sources, ensuring data consistency and quality across the organization.

Data Analysis: Collaborate with data scientists and analysts to build and deploy data-driven solutions, including dashboards, reports, and predictive models.

Performance Optimization: Monitor and optimize data processing systems for performance and scalability.

Collaboration: Work closely with cross-functional teams to understand their data needs and provide timely and accurate insights.

Documentation: Document data processes, pipelines, and models to ensure clarity and knowledge sharing within the team.

Technology Stack: Stay updated with the latest trends and technologies in data engineering and analytics, and recommend improvements to the current tech stack.