Lead Data Analytics

Stefanini Group; Argentina; Presencial; ARS 45.297.000 - 90.595.000; Jornada completa

Descripción de la vacante

Stefanini Group is seeking a Data Platform Engineer in Argentina to design and build scalable Snowflake data models and dbt pipelines. You will own the data warehouse layer powering analytics and reporting, while optimizing Snowflake costs and maintaining robust ELT/ETL pipelines from ERP, MES, IoT, and APIs.

The role emphasizes data quality, lineage, and governance, with exposure to orchestration tools like Airflow and a Git-based CI/CD workflow for analytics.

Formación

• Experience designing and building scalable data models in Snowflake using dimensional modeling and Data Vault patterns.
• Experience developing and maintaining dbt projects with testing, data contracts and documentation.
• Ability to optimize Snowflake costs and manage compute/storage, including clustering and resource monitors.
• Design and build robust ELT/ETL pipelines from diverse sources (ERP, MES, IoT, APIs, flat files) into Snowflake/AWS.
• Implement data quality checks, lineage tracking, and governance across analytics.
• Nice to have: Airflow, Snowflake Tasks, DevOps practices for analytics.

Responsabilidades

• Own the data warehouse layer powering analytics and reporting.
• Develop, test, and document dbt models end-to-end (staging, intermediate, mart).
• Design and maintain robust ELT/ETL pipelines from ERP/MES/IoT/APIs into Snowflake/Warehouse.
• Monitor Snowflake costs and optimize compute/storage configurations.
• Champion data governance, quality checks, and data lineage across the organization.

Conocimientos

Data modeling

ETL/ELT pipelines

SQL

Python scripting

Data governance

Testing & data quality

Herramientas

dbt

Snowflake

AWS Glue

AWS Lambda

Airflow

Git

CI/CD

Descripción del empleo

Experience:

• 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.

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.