Senior Data Engineer – Snowflake

Key Responsibilities

Snowflake Development & Architecture

• Design and implement scalable data warehouse solutions using Snowflake.
• Build and manage Snowflake databases, schemas, tables, views, streams, and tasks.
• Implement Snowflake security, RBAC, masking policies, and data governance controls.
• Optimize Snowflake workloads for performance and cost efficiency.
• Support Snowflake account administration and monitoring activities.

Data Engineering & ELT Development

• Design and develop enterprise-grade ETL/ELT pipelines.
• Build scalable batch and near real-time data processing solutions.
• Develop reusable transformation frameworks using SQL and Python.
• Manage ingestion from multiple source systems, APIs, and cloud applications.
• Support data migration and modernization initiatives.

Data Modeling

• Design:
  • Star Schema
  • Snowflake Schema
  • Data Vault Models
  • Dimensional Models
• Develop analytics-ready data marts.
• Support enterprise data warehouse and lakehouse architectures.

Data Quality & Governance

• Implement automated data quality frameworks.
• Define validation, reconciliation, and monitoring processes.
• Support metadata management and lineage tracking.
• Ensure compliance with enterprise data governance standards.

Cloud Platform Engineering

Develop solutions on:

• Snowflake
• AWS
• Azure
• Google Cloud Platform
• Databricks (preferred)

Performance Optimization

• Optimize SQL queries and ELT processes.
• Improve warehouse utilization and query execution performance.
• Implement clustering, partitioning, and workload optimization strategies.
• Reduce operational costs through efficient compute utilization.

Team Leadership

• Mentor junior and mid-level data engineers.
• Conduct code reviews and design reviews.
• Drive engineering best practices and standards.
• Collaborate with Solution Architects and Delivery Leads on enterprise initiatives.

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Required Technical Skills

Snowflake Expertise

• Snowflake Data Warehouse
• Snowflake Streams & Tasks
• Snowpipe
• Snowpark (Preferred)
• Data Sharing
• Secure Views
• Performance Tuning

Internal Snowflake-focused profiles specifically reference Snowflake architecture, streams, tasks, and transformation frameworks.

Programming

• Advanced SQL
• Python
• Shell Scripting

Data Engineering

• ETL / ELT Development
• Data Warehousing
• Data Modeling
• Data Pipelines
• API Integration

Cloud Platforms

At least one:

• AWS
• Azure
• GCP

Orchestration & DevOps

• Airflow
• Azure Data Factory
• GitHub/GitLab
• CI/CD Pipelines
• Terraform (Preferred)

Data Transformation

• DBT (Preferred)
• Data Validation Frameworks
• Data Quality Automation

Internal profiles indicate experience with DBT-based transformations, Snowflake development, and cloud-native data engineering practices.

---

Preferred Skills

• Databricks
• PySpark
• Kafka
• BigQuery
• Redshift
• Informatica
• Semantic Layer concepts
• AI/ML Data Pipelines

Internal data engineering profiles reference Databricks, BigQuery, PySpark, AI-enabled analytics, and modern data platform architectures.

---

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, Mathematics, or related field.
• 6–10 years of overall IT experience.
• 3+ years of hands-on Snowflake development experience.
• Experience delivering enterprise-scale data engineering projects.

---

Preferred Certifications

• Snowflake SnowPro Core Certification
• Snowflake SnowPro Advanced Certification
• Microsoft Azure Data Engineer Associate
• AWS Certified Data Engineer
• Google Professional Data Engineer

Internal Snowflake practitioners reference SnowPro certifications as relevant credentials.

---

Key Competencies

• Data Architecture
• Problem Solving
• Stakeholder Management
• Team Mentoring
• Solution Design
• Performance Optimization
• Communication & Leadership Key Responsibilities

• Snowflake Development & Architecture

• Design and implement scalable data warehouse solutions using Snowflake.
• Build and manage Snowflake databases, schemas, tables, views, streams, and tasks.
• Implement Snowflake security, RBAC, masking policies, and data governance controls.
• Optimize Snowflake workloads for performance and cost efficiency.
• Support Snowflake account administration and monitoring activities.

• Data Engineering & ELT Development

• Design and develop enterprise-grade ETL/ELT pipelines.
• Build scalable batch and near real-time data processing solutions.
• Develop reusable transformation frameworks using SQL and Python.
• Manage ingestion from multiple source systems, APIs, and cloud applications.
• Support data migration and modernization initiatives.

• Data Modeling

• Design:
  • Star Schema
  • Snowflake Schema
  • Data Vault Models
  • Dimensional Models
• Develop analytics-ready data marts.
• Support enterprise data warehouse and lakehouse architectures.

• Data Quality & Governance

• Implement automated data quality frameworks.
• Define validation, reconciliation, and monitoring processes.
• Support metadata management and lineage tracking.
• Ensure compliance with enterprise data governance standards.

• Cloud Platform Engineering

• Develop solutions on:

• Snowflake
• AWS
• Azure
• Google Cloud Platform
• Databricks (preferred)

• Performance Optimization

• Optimize SQL queries and ELT processes.
• Improve warehouse utilization and query execution performance.
• Implement clustering, partitioning, and workload optimization strategies.
• Reduce operational costs through efficient compute utilization.

• Team Leadership

• Mentor junior and mid-level data engineers.
• Conduct code reviews and design reviews.
• Drive engineering best practices and standards.
• Collaborate with Solution Architects and Delivery Leads on enterprise initiatives.

• ---

• Required Technical Skills

• Snowflake Expertise

• Snowflake Data Warehouse
• Snowflake Streams & Tasks
• Snowpipe
• Snowpark (Preferred)
• Data Sharing
• Secure Views
• Performance Tuning

• Internal Snowflake-focused profiles specifically reference Snowflake architecture, streams, tasks, and transformation frameworks.

• Programming

• Advanced SQL
• Python
• Shell Scripting

• Data Engineering

• ETL / ELT Development
• Data Warehousing
• Data Modeling
• Data Pipelines
• API Integration

• Cloud Platforms

• At least one:

• AWS
• Azure
• GCP

• Orchestration & DevOps

• Airflow
• Azure Data Factory
• GitHub/GitLab
• CI/CD Pipelines
• Terraform (Preferred)

• Data Transformation

• DBT (Preferred)
• Data Validation Frameworks
• Data Quality Automation

• Internal profiles indicate experience with DBT-based transformations, Snowflake development, and cloud-native data engineering practices.

• ---

• Preferred Skills

• Databricks
• PySpark
• Kafka
• BigQuery
• Redshift
• Informatica
• Semantic Layer concepts
• AI/ML Data Pipelines

• Internal data engineering profiles reference Databricks, BigQuery, PySpark, AI-enabled analytics, and modern data platform architectures.

• ---

• Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, Mathematics, or related field.
• 6–10 years of overall IT experience.
• 3+ years of hands-on Snowflake development experience.
• Experience delivering enterprise-scale data engineering projects.

• ---

• Preferred Certifications

• Snowflake SnowPro Core Certification
• Snowflake SnowPro Advanced Certification
• Microsoft Azure Data Engineer Associate
• AWS Certified Data Engineer
• Google Professional Data Engineer

• Internal Snowflake practitioners reference SnowPro certifications as relevant credentials.

• ---

• Key Competencies

• Data Architecture
• Problem Solving
• Stakeholder Management
• Team Mentoring
• Solution Design
• Performance Optimization
• Communication & Leadership