Data Architect Snowflake, Coalesce, CDC, ADF | Remote

Location:

100% Remote

Type: :
Long Term Contract

• 10+ years of experience in Data Engineering, Data Warehousing, or Data Architecture.
• 5+ years of hands-on Snowflake experience.
• Strong experience implementing Coalesce-based transformations and data modeling.
• Proven expertise in CDC and incremental processing frameworks.
• Experience designing modern cloud-native data architectures.
• Experience with Git-based deployment and CI/CD practices.
• Strong understanding of Data Governance and Data Quality frameworks.

• Snowflake
• Snowflake Data Warehouse Architecture
• Streams & Tasks
• Dynamic Tables
• Data Sharing
• Incremental Data Processing
• Performance Tuning and Optimization
• Snowflake Security and Access Controls
• Coalesce
• Coalesce Transform
• Coalesce Catalog
• Coalesce Quality
• Metadata-Driven Data Pipelines
• Data Lineage and Impact Analysis
• Snowflake Integration
• Git-Based Deployment and Version Control
• SQL
• Advanced SQL Development
• Complex Joins
• CTEs and Recursive Queries
• Window Functions
• Analytical and Reporting Queries
• Query Optimization and Performance Tuning
• Python
• API Integration and Data Ingestion
• Data Processing and Transformation
• Workflow Automation
• ETL/ELT Framework Development
• Error Handling and Monitoring
• CDC (Change Data Capture)
• CDC Architecture and Design
• Delta Load Processing
• Timestamp-Based Incremental Loads
• Snowflake Streams-Based CDC
• Real-Time and Batch Data Processing
• Azure Data Factory (ADF)
• Pipeline Development
• Data Orchestration
• Parameterization
• Monitoring and Troubleshooting
• Integration Runtime Configuration
• Enterprise Data Integration Solutions

• Design enterprise data architectures leveraging Snowflake and Coalesce.
• Develop scalable and reusable data pipelines using ADF and Python.
• Implement CDC frameworks and incremental data processing strategies.
• Build metadata-driven and lineage-aware data transformation solutions.
• Optimize Snowflake performance, scalability, and cost efficiency.
• Establish data governance, data quality, and monitoring frameworks.
• Collaborate with business stakeholders, architects, and engineering teams to define data strategies.
• Implement CI/CD deployment processes using Git and Dev Ops best practices.
• Support data integration across multiple source systems and cloud platforms.
• Drive architecture reviews and technical best practices across data programs.