Cloud Data Solutions Architect

3 days ago

Dindigul, Tamil Nadu, India beBeeData Full time ₹ 1,20,00,000 - ₹ 2,50,00,000
Cloud Data Solutions Engineer

We are seeking an experienced Cloud Data Solutions Engineer to join our team.

  • Key Responsibilities:
  • Design and implement scalable data pipelines using cloud-based technologies.
  • Collaborate with cross-functional teams to ensure data quality and availability.
  • Develop and optimize SQL queries for efficient data transformation.
  • Implement data governance and security measures to protect sensitive information.
  • Create interactive dashboards to provide business insights.
  • Maintain and improve existing data solutions to meet evolving business needs.
  • Troubleshoot data issues and resolve them in a timely manner.

Required Skills and Qualifications:

  • Bachelor's degree in Computer Science, Data Science, or related field (Master's preferred).
  • Strong hands-on experience with cloud-based technologies such as AWS, Azure, or Google Cloud.
  • Proficient in programming languages such as Python, Java, or C++.
  • Familiarity with data warehousing and data modeling concepts.
  • Excellent problem-solving and communication skills.

Benefits of Working with Us:

  • Opportunity to work on cutting-edge projects and technologies.
  • Promising career growth and professional development opportunities.
  • A collaborative and dynamic work environment.

About the Role:

  • This is a full-time position that requires occasional travel.
  • The ideal candidate will have a strong passion for cloud computing and data engineering.
  • We offer competitive compensation and benefits packages.
Data Engineering Certifications: A Plus

AWS certifications in data engineering are highly valued but not required.

Familiarity with Big Data Technologies: A Bonus

Knowledge of big data technologies like Hadoop and Spark is a plus but not necessary.

Preferred Skills:

  • Databricks and its ecosystem.
  • Data warehousing and data modeling concepts.
Main Duties Include:
  • Building robust ETL pipelines.
  • Collaborating with stakeholders to ensure data quality.
  • Writing and optimizing SQL queries.
  • Using Git for version control and code collaboration.
  • Designing and managing scalable data lakes.
  • Working with Amazon DynamoDB and Redshift for high-performance data storage.
  • Creating interactive dashboards using Amazon QuickSight.
  • Automating workflows with AWS Step Functions, Lambda, and EventBridge.
  • Monitoring and optimizing data workflows for performance and scalability.

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