Details:

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:
1. Expert-level Snowflake skills: data modeling, performance tuning, query optimization, Snowpark, stored procedures, streams/tasks,



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

- Domain Knowledge (Nice to have): Understanding of manufacturing data ecosystems, ERP/MES systems, and operational data flows. Familiarity with cost accounting, production planning, or supply chain data is a strong plus.
- Problem-Solving Skills: Ability to translate ambiguous business requirements into well-structured data models and scalable data solutions. Strong analytical thinking and attention to detail.
- Collaboration & Communication: Excellent communication skills to work effectively with diverse teams — from plant floor operators to finance teams to senior leadership.



Ability to explain technical concepts to non-technical audiences.

dashboards and reports delivered to business stakeholders.

- Technical Prowess:
1. 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.
2. Working knowledge of SQL for querying Snowflake: JOINs, CTEs, window functions, aggregations, and basic data transformation.
3. 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.
4. Familiarity with data modeling concepts: star schemas, fact/dimension tables, and how they translate into efficient dashboard data sources.
5. Basic Python skills for data wrangling, automation, or extending analytics workflows (pandas, notebooks).

- Domain Knowledge (Nice to have): Understanding of SAP data across various domains such Finance, Operations, Quality etc. Experience developing KPIs such as production metrics, cost tracking, supply chain KPIs, or ERP/MES data. Familiarity with financial reporting or cost-per-unit analysis is a strong plus.

Details:

Nice-to-Haves:

- 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.

📌 Data Analytics Engineer (México)
🏢 Stefanini Group
📍 México