About the job

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.

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.
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.
Strong proficiency in Tableau and/or Power BI: calculated fields, LOD expressions (Tableau), DAX measures (Power BI), parameters, dynamic filters, row-level security, and performance optimization.
Working knowledge of SQL for querying Snowflake: JOINs, CTEs, window functions, aggregations, and basic data transformation.
Foundational understanding of dbt: ability to read and modify existing models, understand the staging/mart layer structure, and run dbt commands for testing and documentation.
Familiarity with data modeling concepts: star schemas, fact/dimension tables, and how they translate into efficient dashboard data sources.
Basic Python skills for data wrangling, automation, or extending analytics workflows (pandas, notebooks).

Nice to have:

Knowledge of data orchestration tools (Airflow, Snowflake Tasks).
Experience with Git-based workflows, CI/CD pipelines, and DevOps practices for analytics.
Familiarity with Snowflake cost governance features: resource monitors, warehouse scheduling, query tagging, and usage dashboards.
Exposure to data mesh or data product concepts