Senior Technical Lead

Job Summary

Highly skilled Data Engineer with strong expertise in Snowflake and dbt should possess extensive hands-on experience in designing, building, optimizing, and maintaining modern cloud-based data platforms. The candidate must have a strong foundation in Data Warehousing, ETL/ELT processes, Data Modeling, SQL, and RDBMS technologies along with the ability to develop scalable and high-performing data solutions.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Snowflake and dbt.
• Build and optimize data warehouse solutions to support analytics and reporting requirements.
• Develop ELT/ETL frameworks and data transformation processes using industry best practices.
• Design and implement dimensional and normalized data models.
• Write Complex SQL queries when required.
• Create reusable dbt models, macros, tests, snapshots, and documentation.
• Optimize Snowflake performance, storage, and compute costs.
• Develop and maintain data ingestion frameworks from various source systems.
• Implement data quality checks, monitoring, and governance controls.
• Collaborate with business analysts, architects, and stakeholders to translate business requirements into technical solutions.
• Troubleshoot and resolve production issues related to data pipelines and warehouse performance.
• Ensure adherence to security, compliance, and data management standards.

Skill Requirements

Snowflake

• Strong hands-on experience in Snowflake architecture and administration.
• Expertise in:
• Virtual Warehouses
• Snowflake Storage & Compute Optimization
• Complex Query writing and Performance Tuning
• Clustering & Partitioning Strategies
• Snowpipe
• Streams & Tasks
• Time Travel & Fail-safe
• Data Sharing
• Role-Based Access Control (RBAC)
• Experience with Snowflake migration projects and performance optimization.

dbt

• Extensive hands-on experience in:
• dbt Core and/or dbt Cloud
• dbt Models
• Macros
• Seeds
• Snapshots
• Incremental Models
• CI/CD Integration
• Data Lineage and Governance

Data Warehousing

• Strong understanding of:
• Dimensional Modeling
• Star and Snowflake Schemas
• Data Lake and Data Warehouse Architectures
• SCD Type 1 & Type 2 Implementations

ETL / ELT

• Extensive experience in designing and implementing ETL/ELT solutions.
• Expertise in handling complex data transformations and large-volume data processing.

Databases

Strong knowledge of multiple RDBMS platforms including:

• Oracle/SQL Server/PostgreSQL etc..

Other Requirements

Good to Have Skills

• Cloud platforms: AWS, Azure, or GCP.
• Data integration tools such as Informatica, Matillion, Fivetran, Talend, or Azure Data Factory.
• CI/CD tools such as GitHub, GitLab, Azure DevOps, Jenkins.
• Exposure to data governance and data quality frameworks.