Senior Data Engineer - Snowflake DB & Data Build Tool

Responsibilities :

- Architect and implement modular, test-driven ELT pipelines using dbt on Snowflake.

- Design layered data models (e.g., staging, intermediate, mart layers / medallion architecture) aligned with dbt best practices.

- Lead ingestion of structured and semi-structured data from APIs, flat files, cloud storage (Azure Data Lake, AWS S3), and databases into Snowflake.

- Optimize Snowflake for performance and cost : warehouse sizing, clustering, materializations, query profiling, and credit monitoring.

- Apply advanced dbt capabilities including macros, packages, custom tests, sources, exposures, and documentation using dbt docs.

- Orchestrate workflows using dbt Cloud, Airflow, or Azure Data Factory, integrated with CI/CD pipelines.

- Define and enforce data governance and compliance practices using Snowflake RBAC, secure data sharing, and encryption strategies.

- Collaborate with analysts, data scientists, architects, and business stakeholders to deliver validated, business-ready data assets.

- Mentor junior engineers, lead architectural/code reviews, and help establish reusable frameworks and standards.

- Engage with clients to gather requirements, present solutions, and manage end-to-end project delivery in a consulting setup

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.

Technical Skills :

Cloud Data Warehouse & Transformation Stack :

- Expert-level knowledge of SQL and Snowflake, including performance optimization, storage layers, query profiling, clustering, and cost management.

- Experience in dbt development : modular model design, macros, tests, documentation, and version control using Git.

Orchestration and Integration :

- Proficiency in orchestrating workflows using dbt Cloud, Airflow, or Azure Data Factory.

- Comfortable working with data ingestion from cloud storage (e.g., Azure Data Lake, AWS S3) and APIs.

Data Modelling and Architecture :

- Dimensional modelling (Star/Snowflake schemas), Slowly changing dimensions.

- Knowledge of modern data warehousing principles.

- Experience implementing Medallion Architecture (Bronze/Silver/Gold layers).

- Experience working with Parquet, JSON, CSV, or other data formats.

Programming Languages :

- Python : For data transformation, notebook development, automation.

- SQL : Strong grasp of SQL for querying and performance tuning.

- Jinja (nice to have) : Exposure to Jinja for advanced dbt development.

Data Engineering & Analytical Skills :

- ETL/ELT pipeline design and optimization.

- Exposure to AI/ML data pipelines, feature stores, or MLflow for model tracking (good to have).

- Exposure to data quality and validation frameworks.