Snowflake Engineer - Azure Data Factory

Experience : 3 to 5 Years.

Qualifications :

Required Qualifications :

- 35 years in data engineering; 2+ years hands-on with Snowflake and dbt.

- Proven experience building and deploying dbt models in production.

- Expert SQL skills and strong understanding of ELT principles.

- Experience with Git, CI/CD, and team-based deployment workflows.

- Familiarity with data quality and validation practices (e.g., dbt tests and dbt docs).

Preferred Qualifications :

- Experience with data modeling (Kimball or dimensional approaches).

- Familiarity with orchestration tools such as dbt Cloud, Airflow, or Azure Data Factory.

- Experience optimizing Snowflake performance (clustering, materializations, query tuning).

Job Description :

We are looking for an experienced and results-driven Data Engineer to join our growing Data Engineering team.

The ideal candidate will be proficient in building scalable, high-performance data transformation pipelines using Snowflake and dbt and be able to effectively work in a consulting setup.

In this role, you will be instrumental in ingesting, transforming, and delivering high-quality data to enable data-driven decision-making across the clients organization.

Key Responsibilities :

- Design and implement scalable ELT pipelines using dbt on Snowflake, following industry accepted best practices.

- Build ingestion pipelines from various sources including relational databases, APIs, cloud storage and flat files into Snowflake.

- Implement data modelling and transformation logic to support layered architecture (e.g., staging, intermediate, and mart layers or medallion architecture) to enable reliable and reusable data assets.

- Leverage orchestration tools (e.g., Airflow,dbt Cloud, or Azure Data Factory) to schedule and monitor data workflows.

- Apply dbt best practices : modular SQL development, testing, documentation, and version control.

- Perform performance optimizations in dbt/Snowflake through clustering, query profiling, materialization, partitioning, and efficient SQL design.

- Apply CI/CD and Git-based workflows for version-controlled deployments.

- Contribute to growing internal knowledge base of dbt macros, conventions, and testing frameworks.

- Collaborate with multiple stakeholders such as data analysts, data scientists, and data architects to understand requirements and deliver clean, validated datasets.

- Write well-documented, maintainable code using Git for version control and CI/CD processes.

- Participate in Agile ceremonies including sprint planning, stand-ups, and retrospectives.

- Support consulting engagements through clear documentation, demos, and delivery of client-ready solutions.