Database Engineer

Automation Development:

• Design and Implement Automated Database Processes: Develop automation solutions for critical database operations, including maintenance, backups, migrations, and data recovery using Python. This helps to reduce manual workload and improve operational efficiency.

Scripting Monitoring:

• Python Scripting for Monitoring and Alerts: Write Python scripts to monitor database health, detect errors, and trigger alerts to ensure databases remain available and perform optimally. This includes setting up error detection, logging, and response mechanisms to prevent downtime.

Cloud and DevOps Tools Integration:

• Automation with Cloud and DevOps Tools: Leverage cloud technology and DevOps tools like Jenkins and Chef to automate Database Engineer resource management tasks. This might include provisioning databases, scheduling backups, and deploying updates in a consistent and automated manner.

Data Integration and ETL Management:

• ETL Pipeline Development: Design and maintain ETL (Extract, Transform, Load) pipelines that transfer data between various systems, ensuring data is accurately moved, transformed, and loaded as per business requirements.

Optimization and Maintenance:

• Performance Tuning: Continuously improve the performance of automation scripts and SQL queries to optimize processing speed, efficiency, and resource usage. Regularly analyze and refine automated processes to support smooth database operations.

Collaboration and Teamwork:

• Collaborate with DBAs and Development Teams: Work closely with Database Administrators (DBAs) and development teams to ensure automation aligns with business objectives and operational needs, supporting an integrated approach to database management.

Compliance and Security:

• Data Protection and Compliance Implementation: Design automation solutions that meet compliance standards, securing data in line with company policies and industry regulations. This includes implementing safeguards for data handling and ensuring processes are audit-ready.

Qualifications:

Experience:

• 3-7 Years in Database Engineering or Automation: At least two years of experience working in database engineering, automation, or Python scripting, with a focus on improving database operations through automation.

Proficiency in Python:

• Python for Automation and Integration: Demonstrated expertise in Python, particularly for automating database tasks and managing data integrations across systems.

Database Administration:

• Hands-On with Relational or NoSQL Databases: Practical experience with at least one database system, such as MySQL, PostgreSQL, MongoDB, or similar, including familiarity with basic administration tasks.

Cloud Platform Familiarity:

• AWS, Azure, or GCP Experience: Foundational knowledge of cloud platforms and experience using database services (such as RDS, DynamoDB) to deploy, manage, and scale databases in a cloud environment.

Version Control and CI/CD:

• Git and CI/CD Pipeline Experience: Competency in using version control systems like Git, as well as experience setting up or working with CI/CD pipelines to ensure smooth, automated deployments and code consistency.

This role requires a strong background in Python automation, database operations, cloud integration, and compliance. The ideal candidate should be proactive, detail-oriented, and comfortable collaborating across teams to support robust database management solutions.