Snowflake Data Engineer

Capgemini; Hyderabad, Pune District, Bengaluru; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Capgemini seeks a skilled Data Engineer to design and implement modern data warehousing for global clients. You will work on end-to-end ELT pipelines, from ingestion to modeling and performance tuning, collaborating with client stakeholders.

Focus areas include developing dbt models on Snowflake, implementing dimensional models, and ensuring data accuracy through tests and documentation. You will drive CI/CD for dbt projects, orchestrate pipelines with Airflow or Dagster, and partner with

Qualifications

• 4+ years in data engineering, with at least 2 years of hands-on dbt experience in production.
• Strong SQL skills, including CTEs, window functions, and query optimization.
• Hands-on Snowflake experience: virtual warehouses, RBAC, Snowpipe, streams and tasks, and performance tuning.
• Experience with Git-based workflows and CI/CD.
• Python for scripting and automation.
• Experience with at least one cloud platform (AWS, Azure, or GCP).

Responsibilities

• Design, build, and maintain modular dbt models (staging, intermediate, and mart) on Snowflake.
• Develop and manage ELT pipelines ingesting data from databases, APIs, and SaaS tools using Fivetran, Airbyte, Snowpipe, or similar.
• Implement dimensional models (star schema, fact and dimension tables), incremental models, and SCD Type 2 using dbt snapshots.
• Write dbt tests, documentation, and source freshness checks to ensure data accuracy.
• Optimize Snowflake performance and cost via warehouse sizing, clustering, and query profiling.
• Manage Snowflake objects including RBAC, streams, tasks, Time Travel, and zero-copy cloning.
• Set up and maintain CI/CD for dbt projects using Git/GitHub Actions, Azure DevOps, or dbt Cloud.
• Orchestrate pipelines using Airflow, dbt Cloud jobs, or Dagster.
• Partner with analysts to translate requirements into scalable data models.
• Take part in code reviews and help define data engineering standards and best practices.

Skills

Data engineering

SQL

Python

dbt

Cloud platforms (AWS/Azure/GCP)

Tools

Snowflake

dbt Core/Cloud

Airflow

Dagster

Git + CI/CD

Fivetran/Airbyte/Snowpipe

dbt_utils

Job description

We are looking for a skilled Data Engineer with strong hands-on experience in Dbt and Snowflake to deliver modern data warehousing solutions for our global clients. You will work on end-to-end implementations, from data ingestion and modeling to performance tuning, in collaboration with client stakeholders.

Key Responsibilities:

• Design, build, and maintain modular dbt models (staging, intermediate, and mart layers) on Snowflake.
• Develop and manage ELT pipelines that ingest data from sources like databases, APIs, and SaaS tools using Fivetran, Airbyte, Snowpipe, or similar.
• Implement dimensional models (star schema, fact and dimension tables), incremental models, and SCD Type 2 using dbt snapshots.
• Write dbt tests, documentation, and source freshness checks to make sure data is accurate and traceable.
• Optimize Snowflake performance and cost through warehouse sizing, clustering, query profiling, and resource monitors.
• Manage Snowflake objects and features, including RBAC, streams, tasks, stored procedures, Time Travel, and zero-copy cloning.
• Set up and maintain CI/CD for dbt projects using Git and GitHub Actions, Azure DevOps, or dbt Cloud.
• Orchestrate pipelines using Airflow, dbt Cloud jobs, or Dagster.
• Partner with analysts and business teams to translate requirements into scalable data models.
• Take part in code reviews and help define data engineering standards and best practices.

Must-Have Skills

• 4+ years in data engineering, with at least 2 years of hands-on dbt (Core or Cloud) experience in production.
• Strong SQL skills, including CTEs, window functions, and query optimization.
• Hands-on Snowflake experience: virtual warehouses, RBAC, Snowpipe, streams and tasks, and performance tuning.
• Working knowledge of Jinja, dbt macros, and packages like dbt_utils.
• Solid understanding of data modeling (Kimball methodology, SCDs, fact and dimension design).
• Experience with Git-based workflows and CI/CD.
• Python for scripting and automation.
• Experience with at least one cloud platform (AWS, Azure, or GCP).