Associate Staff Engineer - Data Engineer

On-siteSeniorData EngineerJust posted

✦Role summaryAI-generated

You will architect and develop scalable Snowflake data models using dimensional and Data Vault patterns, owning the data warehouse layer that powers analytics across the organization. Additionally, you will build and maintain dbt projects, enforce CI/CD best practices, optimize Snowflake costs, and design robust ELT/ETL pipelines to ingest and transform data from diverse sources.

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.