Azure Data Engineer - Freelancer
New
J
JobgetherData engineering
Based in IndiaContractSenior
Salary not disclosed
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Job Details
- Experience
- 8–10 years of professional experience in data engineering or a related discipline.
- Required Skills
- PythonSQLMicrosoft AzureAzure DevOpsPySpark
Requirements
- Hold a bachelor's or master's degree in Computer Science, Information Systems, or a related field.
- Have 8–10 years of professional experience in data engineering or a related discipline.
- Bring strong programming and data manipulation skills, particularly in Python, SQL, and PySpark.
- Have hands-on familiarity with Microsoft Azure data services, including Azure Databricks, Azure Data Factory (ADF), Azure Data Lake, and Azure Synapse.
- Know Delta Lake, data warehousing, Spark architecture, and cloud-based data solution implementation.
- Have experience designing and managing ETL frameworks, data pipelines, and data warehousing solutions.
- Understand data governance, data quality, and data integration principles.
- Be familiar with Azure DevOps, modern CI/CD practices, coding standards, and quality assurance processes.
- Be able to troubleshoot complex data engineering challenges and incorporate data security, privacy, and compliance requirements into technical solutions.
- Maintain an active sole proprietorship suitable for B2B cooperation throughout the agreement.
- Relevant cloud certifications are an advantage; consulting experience and an external customer focus are preferred.
Responsibilities
- Provide technical expertise and direction in data engineering, recommending appropriate tools, technologies, methodologies, and industry best practices.
- Collaborate with stakeholders to understand business and technical requirements, define project scope, and develop project plans.
- Support project managers in delivering projects on time, within budget, and according to established quality standards.
- Monitor project progress, identify potential risks, and implement mitigation strategies.
- Design and oversee scalable data architectures, including data modeling, database design, and data integration.
- Establish coding standards and conduct code reviews to maintain high-quality, maintainable code.
- Improve quality assurance processes for data pipelines and workflows.
- Optimize data processing and storage for performance, operational efficiency, and cost-effectiveness.
- Evaluate and implement technologies and practices that enhance CI/CD, quality assurance, and coding standards.
- Maintain technical documentation and ensure data engineering solutions comply with security, privacy, and governance requirements.
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