Snowflake

About the job

Join a collaborative data engineering team where your work directly enables faster, smarter decisions across the business. In this role, you’ll help build and optimize modern cloud data solutions on Snowflake, partnering closely with analytics, engineering, and stakeholders to deliver reliable, scalable datasets. You’ll contribute to well-modeled data layers using dbt, promote strong engineering practices, and continuously improve performance, cost efficiency, and data quality. If you enjoy turning raw data into trusted, analytics-ready assets—and like working in a culture that values ownership, learning, and teamwork—this is a great opportunity to grow your impact while working with modern data stack technologies.

Responsibilities

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.

Requirements

Good to have skills: Airflow, Azure Data Factory, AWS Glue, Terraform, Power BI

Additional:

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.