Data Engineer

Key Responsibilities

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

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

3. 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..

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

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

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

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

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

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

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

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

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

Required Qualifications

• 3 to 5 years of experience in data engineering roles, with 2+ years of hands-on experience in Snowflake and DBT.

Experience building and deploying DBT models in a production environment.

Expert-level SQL and strong understanding of ELT principles. Strong understanding of ELT patterns and data modelling (Kimball/Dimensional preferred).

• Familiarity with data quality and validation techniques: dbt tests, dbt docs etc.

Experience with Git, CI/CD, and deployment workflows in a team setting

Familiarity with orchestrating workflows using tools like dbt Cloud, Airflow, or Azure Data Factory.

Core Competencies:

o Data Engineering and ELT Development:

• Building robust and modular data pipelines using dbt.
• Writing efficient SQL for data transformation and performance tuning in Snowflake.
• Managing environments, sources, and deployment pipelines in dbt.

o Cloud Data Platform Expertise:

• Strong proficiency with Snowflake: warehouse sizing, query profiling, data loading, and performance optimization.
• Experience working with cloud storage (Azure Data Lake, AWS S3, or GCS) for ingestion and external stages.

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Technical Toolset:

o Languages & Frameworks:

• Python: For data transformation, notebook development, automation.
• SQL: Strong grasp of SQL for querying and performance tuning.

Best Practices and Standards:

o Knowledge of modern data architecture concepts including layered architecture (e.g., staging → intermediate → marts, Medallion architecture).

Familiarity with data quality, unit testing (dbt tests), and documentation (dbt docs).

Security & Governance:

o Access and Permissions:

• Understanding of access control within Snowflake (RBAC), role hierarchies, and secure data handling.
• Familiar with data privacy policies (GDPR basics), encryption at rest/in transit.

Deployment & Monitoring:

o DevOps and Automation:

• Version control using Git, experience with CI/CD practices in a data context.
• Monitoring and logging of pipeline executions, alerting on failures.

Job Type: Full-time

Application Question(s):

• Experience in Snowflake and DBT (Please mention separately ) ?
• Experience in Medallion Architecture? (all layers) (in years) ?
• Experience in SQL and Python? (Please mention separately) ?
• Experience in cloud storage (Azure Data Lake, AWS S3, or GCS)? (preferably Azure Data Lake) ?
• Experience in Data Governance and security? ?
• Open to relocate to Hyderabad? ?

Work Location: In person