
Data Engineer
3 days ago
Experience : 6 to 10 Years.
Qualifications :
Required Qualifications :
- 5 to 8 years of experience in data engineering roles, with 3+ years of hands-on experience working with Snowflake and dbt in production environments.
- 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.
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