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Design, develop, and optimize scalable cloud-based data pipelines and architectures using Azure Data Factory and Snowflake. Transform raw data into analytics-ready datasets using dbt while ensuring data quality and cost efficiency.
Benefits:
- Dental insurance
- Health insurance
- Paid time off
- Training & development
- Vision insurance
Rysun Labs (formerly KCS – Krish Compusoft Services) is an AI, Data & Digital innovation partner of choice for enterprises. Rysun guides and accelerates the AI & Data strategy and Digital Transformation programs for Fortune 2000 enterprises and product startups to shape remarkable customer experiences and intelligent operations. The team delivers innovative, specialized solutions that help High-tech, Retail & Ecommerce, and Energy companies to outperform competition and lead the change in their industry. Rysun partners with Microsoft, Google and AWS to bring the best of enterprise technology to its customers. Rysun believes in quality-first and is CMMI Level 5, ISO 9001 & 27001 certified. The team has a growth mindset fueled by a strong culture of collaboration that unifies its global teams across US, UK, India & South Africa (a proud Level 2 B-BBEE Contributor).
We are seeking a skilled Data Engineer to design, develop, and optimize scalable cloud-based data solutions on Microsoft Azure. The ideal candidate will have strong expertise in Azure data services, data ingestion frameworks, modern data warehousing, and ELT/ETL development using dbt and Snowflake.
The role involves building robust data pipelines, transforming raw data into analytics-ready datasets, implementing best practices for data engineering, and collaborating with cross-functional teams to deliver high-quality, scalable data solutions.
Job Title: Azure Data Engineer
Location: Remote role
Experience: 6+ yr
Key Responsibilities
- Design, develop, and maintain scalable data pipelines using Azure Data Factory (ADF V2).
- Build automated data ingestion frameworks to process bulk data from multiple source systems into target platforms.
- Implement Watermark and Change Data Capture (CDC) strategies for incremental data loading.
- Design and develop modern Azure cloud data architectures for enterprise-scale applications.
- Create and manage secure, scalable, and cost-effective hosting solutions on Microsoft Azure.
- Develop, optimize, and maintain dbt models, macros, seeds, snapshots, and tests to transform raw data into analytics-ready datasets.
- Work extensively with Snowflake to develop and optimize data models, transformations, and performance.
- Implement version control, documentation, and deployment best practices for dbt projects.
- Handle and process multiple file formats including JSON, Parquet, and other structured or semi-structured data.
- Collaborate with Data Analysts, Data Scientists, Architects, and Business teams to understand data requirements.
- Optimize data pipelines for performance, scalability, reliability, and cost efficiency.
- Ensure data quality by implementing testing, validation, and monitoring mechanisms.
- Troubleshoot and resolve data pipeline and platform-related issues.
- Follow industry best practices for security, governance, and data management.
Required Qualifications
- Bachelor’s degree in computer science, Information Technology, Engineering, or a related field.
- 5–10 years of experience in Data Engineering or Cloud Data Engineering.
- Strong expertise in Microsoft Azure Data Services.
- Hands-on experience with Azure Data Factory (ADF V2).
- Experience designing and implementing:
- Bulk Data Ingestion Frameworks
- Watermark-based Incremental Loads
- Change Data Capture (CDC)
- Strong understanding of Azure cloud architecture and data platform design.
- Experience working with Snowflake as a cloud data warehouse.
- Hands-on experience with dbt (Data Build Tool), including:
- dbt Models
- Macros
- Tests
- Documentation
- Version Control
- Experience processing data in formats such as:
- JSON
- Parquet
- CSV
- Strong SQL programming and query optimization skills.
- Experience with Git-based version control.
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