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Job Description:

We are seeking a highly skilled Senior Snowflake Data Engineer to design, develop, and optimize enterprise data solutions that support analytics, reporting, and business intelligence initiatives across the organization. This role will be responsible for building scalable data pipelines, implementing Snowflake best practices, and ensuring the reliability, performance, and security of critical data assets.

The ideal candidate is a hands-on data engineering professional with deep expertise in Snowflake, cloud-based data platforms, and modern ELT/ETL architectures. This individual will collaborate closely with business stakeholders, data analysts, data scientists, and application development teams to deliver scalable and high-performing data solutions that enable data-driven decision-making.

Required Experience

7+ years of professional experience in Data Engineering, Data Warehousing, or related disciplines
5+ years of hands-on experience with Snowflake in enterprise environments
Proven expertise designing, developing, and supporting large-scale data platforms and modern analytics architectures
Experience working in cloud-based environments and supporting mission-critical business systems

Key Responsibilities

Data Engineering & Pipeline Development

Design, develop, and maintain scalable, high-performance data pipelines and ELT/ETL processes utilizing Snowflake and modern data engineering frameworks.
Build and support enterprise Data Lakes and Data Warehouses.
Develop robust data integration solutions that ensure data quality, consistency, and reliability.
Optimize data movement, transformations, and processing workflows across multiple systems and platforms.
Implement automated and reusable data engineering patterns to improve efficiency and maintainability.

Snowflake Platform Engineering

Architect, develop, and maintain Snowflake-based data solutions.
Design and optimize Snowflake data models to support analytics, reporting, and operational workloads.
Configure and manage Snowflake Tasks, Streams, Virtual Warehouses, RBAC, and data-sharing capabilities.
Monitor and optimize Snowflake performance, storage utilization, and query efficiency.
Implement cost optimization strategies to maximize platform value while maintaining performance standards.

Key Snowflake Technologies:

Snowflake SQL
Streams
Tasks
Virtual Warehouses
Role-Based Access Control (RBAC)
Data Sharing
Clustering Keys
Performance Tuning
Cost Optimization

Data Architecture & Modeling

Design scalable and maintainable data models that support business intelligence, analytics, and operational reporting requirements.
Apply dimensional modeling and data warehousing best practices.
Establish standards and governance frameworks for enterprise data assets.
Contribute to long-term data architecture strategy and platform evolution.

Cloud Data Solutions

Build and support cloud-based data platforms leveraging Azure and/or AWS.
Integrate cloud-native services into enterprise data workflows.
Collaborate with cloud engineering teams to ensure scalability, security, and reliability of data environments.
Optimize cloud resource utilization and operational costs.

Data Quality & Reliability

Monitor the health and performance of data pipelines and platform services.
Troubleshoot and resolve issues related to data accuracy, completeness, latency, and reliability.
Implement data validation, testing, and monitoring processes.
Support root cause analysis and continuous improvement initiatives.

Collaboration & Stakeholder Engagement

Partner with data analysts, data scientists, product owners, and business stakeholders to translate business requirements into effective technical solutions.
Participate in architectural discussions and contribute to technology decisions and development standards.
Communicate technical concepts effectively to both technical and non-technical audiences.
Support cross-functional initiatives focused on advancing enterprise data capabilities.

Required Qualifications

7+ years of professional Data Engineering experience
5+ years of hands-on Snowflake experience in enterprise environments
Strong expertise with:
- Snowflake SQL
- Tasks
- Streams
- Virtual Warehouses
- RBAC
- Clustering
- Performance Optimization
Advanced SQL development skills
Strong experience building modern ETL/ELT data pipelines
Experience with cloud platforms such as Azure and/or AWS
Proficiency in Python for data engineering and automation
Experience with orchestration tools including:
- Azure Data Factory (ADF)
- Apache Airflow
Strong understanding of:
- Data Modeling
- Data Warehousing
- Distributed Data Systems
- Data Integration Architectures
Proven ability to independently own solutions from requirements gathering through deployment and support
Excellent communication, collaboration, and stakeholder management skills

Preferred Qualifications

Experience implementing CI/CD pipelines for data engineering workloads
Experience with:
- Azure DevOps
- GitHub Actions
- Git-based source control practices
Knowledge of modern big data frameworks including:
- Databricks
- Apache Spark
- Delta Lake
Experience supporting high-volume enterprise data platforms with strict performance and availability requirements
Experience implementing:
- Data Governance Frameworks
- Metadata Management Solutions
- Data Quality Programs
- Data Lineage Initiatives
SnowPro Certification(s) preferred
Familiarity with data security, compliance, and access management best practices

Leadership & Communication

Strong written and verbal communication skills.
Ability to document technical designs, data architectures, and operational procedures.
Comfortable interacting with technical teams, business leaders, and executive stakeholders.
Demonstrated ability to mentor junior engineers and share best practices across teams.
Ability to influence architectural decisions and drive continuous improvement initiatives.

Soft Skills & Competencies

Strong problem-solving and analytical abilities.
Ability to translate ambiguous or evolving requirements into scalable technical solutions.
Strong attention to detail and commitment to data quality.
Collaborative, team-oriented mindset with a passion for continuous learning.
High degree of ownership, accountability, and professionalism.
Ability to manage multiple priorities in a fast-paced environment.

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