Data Engineer - Snowflake & Systems Integration

Getsquire; Buenos Aires; Presencial; ARS 1.200.000 - 2.400.000; Jornada completa

Descripción de la vacante

SQUIRE is seeking a Data Engineer to design and maintain data pipelines between CRM, billing, internal platforms, and a Snowflake data warehouse. You will ensure data accuracy, accessibility, and trust across the company, partnering with TechOps and Analytics on cross‑system initiatives and dashboards.

You will develop ELT/ETL processes, model data for analytics, and implement data quality checks while collaborating with non‑technical stakeholders.

Formación

• 5+ years of experience as a data engineer or in an integration/backend role.
• Strong Snowflake experience or equivalent cloud data warehouse; advanced SQL; and Python for data engineering.
• Hands-on experience integrating SaaS business systems via REST APIs, webhooks, and bulk data interfaces.
• Experience with at least one ELT/integration platform (Fivetran, Airbyte, Hightouch, Census, Workato, MuleSoft, or equivalent).
• Experience with a transformation framework (dbt strongly preferred) and workflow orchestration (Airflow, Dagster, Prefect, or similar).
• Solid understanding of data modeling (dimensional, normalized, junction objects); familiarity with reverse ETL patterns, including Polytomic.
• Practical experience with data quality, deduplication, and master data management (MDM) concepts.
• Experience with Git and CI/CD pipelines; strong problem‑solving skills and ability to debug across systems, networks, and APIs.
• Excellent communication and collaboration skills; this role works directly with non‑technical cross‑functional stakeholders.
• Bachelor's degree in Computer Science or Engineering.
• English proficiency is required; must be based in Buenos Aires with availability to work on‑site in our office in CABA two days a week (Tuesdays and Thursdays).

Responsabilidades

• Design, build, and maintain bidirectional data pipelines between operational systems (CRM, billing, internal platforms, marketing/sales/CX tools) and Snowflake.
• Build integrations that surface data from internal systems back into CRM and other downstream business tools as a reliable source of truth.
• Partner with TechOps and Analytics & Insights on cross‑system initiatives: data cleanup, schema changes, new tool rollouts, and reporting/dashboard enablement.
• Develop and maintain ELT/ETL processes with clear orchestration, scheduling, monitoring, and alerting.
• Model data in Snowflake to support analytics, reporting (Tableau, Looker), and AI/LLM use cases.
• Implement data quality checks, validation rules, and reconciliation logic to catch drift between systems before it reaches end users.
• Translate business definitions into accurate, scalable data structures.
• Audit existing automations and integrations; rip‑and‑replace where they create duplicate or bad data.
• Build and maintain CI/CD for data pipelines and SQL/transformation code; document data contracts, schemas, and lineage so other teams can self‑serve with confidence.
• Troubleshoot and resolve pipeline failures, data discrepancies, and integration issues across the stack.
• Build and maintain a data dictionary covering key fields, definitions, owners, and source systems, making it easy for any team to understand what data exists, where it lives, and what it means.

Conocimientos

Snowflake

Python

REST APIs

ELT/ETL

dbt

Airflow

Data modeling

Git & CI/CD

Communication

English

Educación

Bachelor's degree in CS or Engineering

Herramientas

BigQuery

Redshift

dbt

Airflow

Dagster

Prefect

Fivetran

Airbyte

Hightouch

MuleSoft

Descripción del empleo

SQUIRE is seeking a Data Engineer to design and maintain data pipelines between CRM, billing, internal platforms, and a Snowflake data warehouse. You will ensure data accuracy, accessibility, and trust across the company, partnering with TechOps and Analytics on cross‑system initiatives and dashboards.

You will develop ELT/ETL processes, model data for analytics, and implement data quality checks while collaborating with non‑technical stakeholders.