Data Engineer

Scale Up Recruiting Partners; Municipio de Rincón de los Sauces; Presencial; ARS 136.174.000 - 211.826.000; Jornada completa

Descripción de la vacante

Scale Up Recruiting Partners is seeking a Data Engineer based in Argentina (remote). You will design, build, and operate scalable AWS data platforms to support analytics, batch and streaming pipelines, and AI/ML workloads.

You will work with Python, SQL, and various AWS services to ensure data accessibility, governance, and quality. You’ll integrate CRM/ERP data and implement robust data quality and observability practices.

Formación

• 4+ years of experience designing and operating AWS data platforms.
• Strong hands-on experience with AWS services (S3, Glue, Lake Formation, Athena, Redshift, EMR, Kinesis/MSK, DynamoDB, OpenSearch, Neptune).
• Strong Python skills with modular, testable, maintainable code.
• Advanced SQL skills including CTEs, window functions, and query optimization.
• Experience with ETL/ELT, data warehouses, lakehouse architectures, and analytical query engines.
• Experience with Spark, Hadoop, Hive, and/or Flink.
• Understanding of distributed data systems, event-driven architectures, fault tolerance, and idempotency.
• Experience with API integrations and enterprise CRM/ERP systems.
• Experience with CloudWatch, CloudTrail, data governance, security, and compliance practices.
• Familiarity with Terraform or CloudFormation.
• Excellent English communication and ability to explain technical trade-offs to stakeholders.

Responsabilidades

• Design, build, and maintain scalable AWS data platforms following Well-Architected best practices.
• Develop and operate batch and streaming data pipelines for analytics and AI/ML use cases.
• Build ingestion, transformation, enrichment, and normalization workflows using internal and external sources.
• Work with structured, semi-structured, unstructured, and graph data.
• Design and maintain data models (normalized, denormalized, graph-based).
• Build and maintain knowledge graphs for advanced analytics and inference.
• Integrate data from CRM/ERP platforms (Salesforce, HubSpot, SAP, NetSuite, Dynamics 365, Workday).
• Establish data governance practices (cataloging, lineage, access control, encryption, auditability).
• Implement data quality, observability, and monitoring for freshness and pipeline health.
• Optimize data architecture for cost and performance (partitioning, indexing, compression, storage tiering).
• Create internal tools, dashboards, and technical docs to enhance visibility and maintainability.

Conocimientos

Python

SQL

Data modeling

Data governance

English communication

Herramientas

AWS

Apache Airflow

Spark

Redshift

OpenSearch

Terraform

CloudWatch

Descripción del empleo

• Eligibility: This opportunity is open exclusively to candidates based in Argentina.

Data Engineer

• Location: Argentina — Remot
• Type: Full-time, long-term contractor
• Eligibility: This opportunity is open exclusively to candidates based in Argentina.

About The Company

Our client is a leading cloud-first consulting company delivering Managed Services, Staffing, and Professional Services. With a remote-first global team across North America, Europe, and Southeast Asia, the company helps organizations embrace cloud technologies through innovative, customer-focused solutions.

The organization values collaboration, agility, innovation, integrity, diversity and inclusion, and offers an environment where talented professionals can work on meaningful, cutting-edge initiatives.

About The Role

Our client is looking to hire a highly skilled Data Engineer to design, build, and operate scalable AWS data platforms supporting analytics, batch and streaming pipelines, and AI/ML workloads.

This role requires strong hands-on AWS and Python expertise, as well as the ability to build reliable data systems, integrate enterprise platforms, and improve data accessibility, governance, and quality across the organization.

What You'll Do

• Design, build, and maintain scalable AWS data platforms aligned with AWS Well-Architected best practices.
• Develop and operate batch and streaming data pipelines for analytics and AI/ML use cases.
• Build ingestion, transformation, enrichment, and normalization workflows using internal systems and external APIs.
• Work with structured, semi-structured, unstructured, and graph data.
• Design and maintain data models, including normalized, denormalized, and graph-based models.
• Build and maintain knowledge graphs that enable advanced analytics and inference.
• Integrate data from CRM and ERP platforms such as Salesforce, HubSpot, SAP, NetSuite, Dynamics 365, and Workday.
• Establish data governance practices around cataloging, lineage, access control, encryption, and auditability.
• Implement data-quality, observability, and monitoring processes for freshness, completeness, and pipeline health.
• Optimize data architecture for cost and performance through partitioning, indexing, compression, and storage tiering.
• Create internal tools, dashboards, and technical documentation to improve visibility and maintainability.

What We’re Looking For

• 4+ years of experience designing and operating AWS data platforms.
• Strong hands-on experience with AWS services such as S3, Glue, Lake Formation, Athena, Redshift, EMR, Kinesis/MSK, DynamoDB, OpenSearch, and Neptune.
• Strong Python skills, with an emphasis on modular, testable, and maintainable code.
• Advanced SQL skills, including CTEs, window functions, and query optimization.
• Experience with ETL/ELT, data warehouses, lakehouse architectures, and analytical query engines.
• Experience with Spark, Hadoop, Hive, and/or Flink.
• Strong understanding of distributed data systems, event-driven architectures, fault tolerance, and idempotency.
• Experience with API integrations and enterprise CRM/ERP systems.
• Experience with CloudWatch, CloudTrail, data governance, security, and compliance practices.
• Familiarity with Terraform or CloudFormation.
• Advanced English communication skills and the ability to explain technical trade-offs to technical and non-technical stakeholders.