Data Engineer - Snowflake & ELT Pipelines

Infosys; Bengaluru; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Infosys seeks a Senior Data Engineer to design, develop, and maintain Snowflake-based data warehouses and analytics models. You will build dbt transformations, implement ELT pipelines, and optimize performance, while ensuring data quality and governance across analytics workloads.

Collaboration with analysts and cross-functional teams in an AGILE environment will be essential to deliver robust data marts and scalable BI assets for business insights.

Qualifications

• 3-5 years of experience in data engineering / data warehousing roles.
• Hands-on Snowflake experience including schema design, SQL development, and performance optimization.
• dbt (Data Build Tool) experience for transformations, testing, and documentation.
• Strong SQL skills for large datasets.
• Structured data modeling concepts and curated analytics layers.
• Qualification: BTECH, MTECH, MCA, or MSC.

Responsibilities

• Design, develop, and maintain Snowflake-based data warehouses.
• 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.

Skills

Cross-functional collaboration

Communication

Agile methodologies

Education

BTECH/MTECH/MCA/MSC

Tools

Snowflake

dbt

SQL

Airflow

Azure Data Factory

AWS Glue

Terraform

Power BI

Job description

Snowflake Good to have skills:

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

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.

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.