Snowflake

Infosys; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Infosys in Bengaluru is seeking a data engineer to design and maintain Snowflake-based data warehouses and ELT pipelines. You will build dbt models, optimize performance, and implement data quality checks. Collaboration with analysts and release management for data changes is required.

The ideal candidate has 3–5 years of experience, Snowflake SQL, and hands-on dbt skills, with strong knowledge of ETL/ELT patterns and cloud data platforms.

Qualifications

• 3–5 years of experience in data engineering / data warehousing.
• Hands-on Snowflake design, SQL development, and performance optimization.
• Hands-on dbt for transformations, testing, and documentation.
• Strong SQL for large datasets.
• Experience with structured data modeling and analytics-ready layers.

Responsibilities

• Design, develop, and maintain Snowflake-based data warehouse solutions aligned to business and analytics needs.
• Build and manage dbt models, including incremental loads, tests, documentation, and modular transformations.
• Develop efficient ELT pipelines to ingest, transform, and curate data into analytics-ready datasets.
• Optimize Snowflake performance and cost through clustering strategies, warehouse sizing, query tuning, and workload management.
• Implement data quality checks, reconciliation processes, and monitoring to ensure trusted and consistent data outputs.
• Collaborate with analysts and downstream consumers to translate requirements into robust data models (facts/dimensions) and marts.
• Support release management for data changes, ensuring version control, code reviews, and reliable deployments.
• Troubleshoot data issues end-to-end, performing root-cause analysis and implementing preventive fixes.

Education

BTECH, MTECH, MCA, or MSC.

Tools

Snowflake

SQL

dbt

Airflow

Azure Data Factory

AWS Glue

Terraform

Power BI

Job description

Good to have skills

Airflow, Azure Data Factory, AWS Glue, Terraform, Power BI

Key Responsibilities

• Design, develop, and maintain Snowflake-based data warehouse solutions aligned to business and analytics needs.
• Build and manage dbt models, including incremental loads, tests, documentation, and modular transformations.
• Develop efficient ELT pipelines to ingest, transform, and curate data into analytics-ready datasets.
• Optimize Snowflake performance and cost through clustering strategies, warehouse sizing, query tuning, and workload management.
• Implement data quality checks, reconciliation processes, and monitoring to ensure trusted and consistent data outputs.
• Collaborate with analysts and downstream consumers to translate requirements into robust data models (facts/dimensions) and marts.
• Support release management for data changes, ensuring version control, code reviews, and reliable deployments.
• Troubleshoot data issues end-to-end, performing root-cause analysis and implementing preventive fixes.

Minimum Qualifications

• BTECH, MTECH, MCA, or MSC.
• 3–5 years of experience in data engineering / data warehousing roles.
• Hands-on experience with Snowflake including schema design, SQL development, and performance optimization.
• Hands-on experience with dbt (Data build Tool) for transformations, testing, and documentation.
• Strong SQL skills with the ability to write optimized, maintainable queries for large datasets.
• Experience working with structured data modeling concepts and building curated layers for analytics consumption.

Preferred Qualifications

• Experience implementing end-to-end ELT patterns using dbt best practices (macros, packages, reusable models, exposures).
• Familiarity with dimensional modeling and designing scalable marts for BI and self-service analytics.
• Experience with CI/CD practices for analytics engineering (automated testing, environment promotion, deployment workflows).
• Strong understanding of data governance concepts such as lineage, access controls, and auditability in cloud data platforms.
• Proven ability to collaborate cross-functionally, communicate clearly, and deliver high-quality data products in agile teams.