Highly Skilled Data Professional

2 days ago

Jaipur, Rajasthan, India beBeeDataEngineer Full time ₹ 1,00,00,000 - ₹ 1,50,00,000

We are seeking a highly skilled professional to fill the role of Data Engineer. As a key member of our team, you will be responsible for designing, implementing and managing complex data pipelines using CI/CD practices.

About This Role
  • This is an exciting opportunity to work with a talented team of professionals who share your passion for innovative solutions and cutting-edge technologies.

The ideal candidate will have extensive experience working with data engineering tools such as Databricks (Spark), Python, GitHub, and SQL. They will also possess excellent problem-solving skills and be able to troubleshoot and resolve complex issues efficiently.

Key Responsibilities
  1. Data Pipeline Design and Implementation: Design and implement CI/CD pipelines using GitHub Actions or equivalent tools to ensure seamless code integration and deployment.
  2. GitHub Configuration and Maintenance: Configure and maintain GitHub branch protection rules and workflows to guarantee smooth code integration and deployment.
  3. Spark Job Development: Build, optimize, and debug Spark jobs in Databricks for large-scale data processing, ensuring high performance and reliability.
  4. Databricks Deployment: Automate Databricks deployments using Databricks Asset Bundles (DABs) or similar frameworks to reduce manual effort and increase efficiency.
  5. Data Pipeline Development: Develop, test, and maintain robust data pipelines using Python and SQL, ensuring accurate and timely data delivery.
  6. Testing and Quality Assurance: Implement unit testing, validation, and quality checks for data workflows, identifying and resolving issues promptly.
  7. Multi-Environment Deployments: Manage multi-environment deployments (Dev, Stage, Prod) to ensure reliability, consistency, and business continuity.
  8. Configuration Files Management: Write and maintain configuration files (YAML, JSON) for workflows, pipelines, and automation, streamlining processes and improving productivity.
  9. Troubleshooting: Troubleshoot and resolve configuration or syntax issues in YAML and related files, minimizing downtime and maximizing uptime.
  10. Data Migrations: Migrate legacy reports into modern, self-serve dashboards (Snowflake, Tableau, Power BI), providing actionable insights and facilitating data-driven decision-making.
  11. Cross-Functional Collaboration: Collaborate with cross-functional teams to understand needs and deliver data models, insights, and reporting that meet their requirements.
  12. Data Governance: Establish guardrails with data contracts, quality checks, and documentation to ensure long-term reliability and maintainability of data assets.

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