Data Engineer

Design and implement data pipelines to collect, clean, and integrate data from various sources.
Extract, transform, and load (ETL) processes to ensure data is accurate and usable.
Collaborate with data scientists, analysts, and other stakeholders to understand data requirements.
Develop, maintain, and optimize relational and non-relational databases.
Create and manage database schemas, tables, and views.
Perform routine database maintenance and updates to ensure efficiency.
Build scalable and reliable data pipelines for batch and real-time data processing.
Monitor pipeline performance and troubleshoot errors or bottlenecks.
Automate repetitive data collection and processing tasks.
Implement data validation and quality checks to ensure accuracy and consistency.
Monitor and improve data quality and integrity over time.
Document data workflows and processes for transparency.
Optimize database queries and data models for faster processing.
Manage large datasets and design scalable storage solutions.
Implement caching and indexing strategies for performance improvement.
Work closely with software engineers, data scientists, and business teams.
Translate business needs into technical requirements.
Document processes and provide regular updates to stakeholders.
Use tools like Python, SQL, Spark, Hadoop, Kafka, and cloud services (AWS, Azure, GCP).
Develop custom scripts and utilities to enhance data operations.
Stay updated on emerging data engineering technologies and practices.
Ensure data storage and processing comply with organizational and legal standards.
Implement data security protocols to protect sensitive information.
Basic knowledge of databases, SQL, and Python.
Familiarity with ETL tools and concepts.
Understanding of data structures and algorithms.