Senior Snowflake Data Engineer
Role Description
- 12+ years of experience as a Data Engineer.
- Design, develop, and maintain scalable, resilient data engineering solutions.
- Strong expertise in Snowflake, Python, PySpark, DBT, Qlik Replicate, and Airflow.
- Experience building enterprise-grade data pipelines, CDC solutions, and cloud data platforms.
- Strong understanding of data governance, security, and DevOps practices.
Must-Have Technical Skills
- 12+ years of hands-on Data Engineering experience.
- Deep expertise in Snowflake , including:
- Snowpipe
- Tasks
- Streams
- Dynamic Tables
- Advanced SQL
- Data Masking Policies
- RBAC
- Performance tuning
- Strong experience building and maintaining DBT Cloud models with:
- Testing
- Documentation
- Data lineage
- Excellent proficiency in:
- Python
- PySpark
- Experience developing ingestion frameworks for:
- Files
- APIs
- Schema validation
- Retry mechanisms
- Metadata capture
- Experience ingesting data from:
- CSV
- Fixed-width files
- Multi-record layouts
- JSON
- XML
- Excel
- Semi-structured data
- Experience designing ingestion patterns for:
- Mainframe VSAM
- EBCDIC data formats
- Strong expertise in:
- Schema drift detection
- Schema evolution
- Metadata-driven architecture
- Hands-on experience with:
- Qlik Replicate
- Change Data Capture (CDC)
- Full-load database replication
- Experience replicating data from:
- Oracle
- SQL Server
- DB2
- Strong understanding of:
- Data governance
- RBAC
- Data access controls
- Snowflake security
- Experience implementing:
- Dynamic data masking
- Conditional masking
- Role-based masking
- Experience with Protegrity or similar tokenization/data protection platforms.
- Hands-on experience with:
- Astronomer Airflow
- Workflow orchestration
- Dependency management
- Scheduling
- Retry logic
- SLA monitoring
- Experience integrating CI/CD pipelines using:
- GitLab
- Azure DevOps
- Strong knowledge of:
- Git branching strategies
- Merge requests
- Code reviews
- Repository management
- Experience implementing:
- Monitoring
- Alerting
- Pipeline observability
- Expertise in optimizing:
- Snowflake compute
- Storage
- Query performance
- Pipeline scalability
Roles & Responsibilities
- Design and develop scalable, resilient data pipelines using Snowflake.
- Build and maintain DBT models with testing, documentation, and lineage.
- Develop Python/PySpark ingestion frameworks for APIs and file-based integrations.
- Design ingestion solutions for structured and semi-structured data sources.
- Implement Mainframe VSAM ingestion for complex EBCDIC data.
- Detect, analyze, and manage schema drift across multiple data sources.
- Develop metadata-driven schema evolution strategies.
- Coordinate schema changes through CI/CD pipelines.
- Configure and maintain Qlik Replicate for CDC and full-load replication.
- Build reliable, auditable, and recoverable replication pipelines.
- Implement Snowflake data masking, RBAC, and governance policies.
- Apply Protegrity tokenization to protect sensitive data.
- Build and schedule workflows using Astronomer Airflow.
- Integrate data pipelines with GitLab and Azure DevOps CI/CD processes.
- Manage source code repositories and perform code reviews.
- Implement monitoring and alerting for ingestion, replication, and transformation pipelines.
- Optimize Snowflake performance, storage utilization, and query execution.
- Scale data pipelines to support increasing data volumes and low-latency processing.
Generic Managerial Skills
- Excellent analytical and problem-solving skills.
- Strong verbal and written communication skills.
- Ability to collaborate with cross-functional engineering and business teams.
- Strong stakeholder management and coordination skills.
- Ability to lead technical discussions and mentor junior engineers.
- Experience working in Agile/Scrum environments.
- Strong ownership, accountability, and commitment to quality delivery.
- Ability to manage multiple priorities in a fast-paced environment.
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