Associate Staff Engineer - Data Engineer

On-siteSeniorData Engineerposted 2w ago

✦Role summaryAI-generated

This role focuses on architecting and maintaining a Snowflake-based data warehouse, leveraging dimensional modeling, Data Vault, and dbt to build scalable data pipelines. The engineer will also optimize Snowflake costs and enforce CI/CD and testing best practices across the data stack.

About this role

Role and Responsibility:

Design & Build Data Models: Architect and develop scalable, performant data models in Snowflake using dimensional modeling (star/snowflake schemas), OBT, and Data Vault patterns.

You''ll own the data warehouse layer that powers analytics and reporting across the organization.

Develop & Maintain dbt Projects: Build, test, and document dbt models end-to-end — staging, intermediate, and mart layers. Enforce best practices including version control, CI/CD, data contracts, and comprehensive testing (schema, data, and freshness tests).
Snowflake Cost Optimization: Monitor and optimize Snowflake compute and storage costs. This includes warehouse sizing and auto-suspend tuning, query profiling, clustering key strategies, materialization choices, and implementing resource monitors to control spend.
Data Pipeline Development: Design and build robust ELT/ETL data pipelines to ingest, transform, and deliver data from diverse sources (ERP, MES, IoT, APIs, flat files) into Snowflake/ AWS Glue/ Lambda. Automate workflows using orchestration tools and Python scripting.
Data Quality & Governance: Implement data quality checks, lineage tracking, and documentation standards. Champion data governance practices to ensure trusted, reliable data across the organization.

Expectation from the Candidate:

Experience: 3+ years of experience in data engineering, analytics engineering, or a related role, with hands-on Snowflake and dbt experience in a production environment.

Technical Prowess:

Expert-level Snowflake skills: data modeling, performance tuning, query optimization, Snowpark, stored procedures, streams/tasks, data sharing, and cost management.
Strong dbt proficiency: model development, Jinja/macros, packages, incremental models, snapshots, exposures, and CI/CD integration.
Advanced SQL skills for complex transformations, window functions, CTEs, and analytical queries.
Proficiency in Python for data pipeline development, automation, scripting, and API integrations.
Experience with dashboard/BI tools (Power BI, Tableau) including data source optimization and DAX/LOD calculations.
Cloud platform experience (Azure, AWS, or GCP) including storage, compute, networking, and infrastructure-as-code basics.