Senior Data Engineer - Hybrid Cloud, Snowflake & Finance

Epam Systems Inc; Buenos Aires; Híbrido; ARS 1.200.000 - 1.800.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive salary package

Career development

Learning opportunities

Hybrid work

Wellbeing programme

Tech communities

Descripción de la vacante

Endava is seeking a seasoned Data Engineer to design, build, and optimize scalable data pipelines and architectures. You will work across ingestion, transformation, validation, and delivery, shaping data models and workflows using Airflow, Snowflake, and DBT on AWS.

You will own complex technical initiatives, mentor engineers, and collaborate with architecture, DevOps, product, and data teams to deliver reliable, cost-efficient data solutions in a hybrid work environment.

Formación

• 5+ years of professional experience in data engineering or a related software engineering role.
• Strong hands-on experience building and supporting production-grade data pipelines.
• Strong knowledge of Apache Airflow, including DAG development, scheduling, dependencies, retries, and monitoring.
• Strong experience with Snowflake or equivalent cloud data warehouse technologies.
• Proven experience with DBT.
• Solid experience with AWS or another major cloud platform.
• Experience with CI/CD and Infrastructure-as-Code practices.
• Strong programming skills in Python and SQL.
• Demonstrated experience troubleshooting complex production issues and performing root cause analysis.
• Ability to independently own complex technical initiatives from design through implementation and production.
• Strong communication, problem-solving, and technical documentation skills.

Responsabilidades

• Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, validation, and delivery.
• Develop reusable, modular, and configuration-driven data processing solutions.
• Contribute to technical architecture and evaluate solutions based on scalability, performance, reliability, and maintainability.
• Design and optimize data models, SQL queries, stored procedures, and data processing workflows.
• Design and maintain Apache Airflow workflows, including scheduling, dependencies, retries, monitoring, and error handling.
• Develop automated data validation and quality checks using DBT and SQL.
• Implement appropriate monitoring, alerting, and observability for critical data pipelines.
• Troubleshoot and resolve complex data processing and quality issues.
• Develop data solutions using AWS and cloud-based data warehouse technologies such as Snowflake.
• Work with cloud services for data storage, processing, compute, messaging, and serverless workloads.
• Support data platforms across development, QA, staging, and production environments.
• Optimize data workloads for performance, scalability, reliability, and cost.
• Develop and maintain CI/CD pipelines for data engineering applications and infrastructure.
• Apply Infrastructure-as-Code practices using Terraform or equivalent technologies.
• Support production deployments, incident resolution, root cause analysis, and continuous improvement.
• Maintain technical documentation, operational runbooks, and troubleshooting guides.
• Take ownership of complex engineering initiatives from design through production.
• Conduct code reviews and promote engineering best practices.
• Mentor junior and mid-level engineers and provide technical guidance.
• Collaborate with architecture, DevOps, product, and other technology teams.

Conocimientos

Python

SQL

Apache Airflow

Snowflake

DBT

AWS

CI/CD

Infrastructure as Code

Communication

Problem solving

Herramientas

Terraform

PySpark

EMR

Descripción del empleo

Endava is seeking a seasoned Data Engineer to design, build, and optimize scalable data pipelines and architectures. You will work across ingestion, transformation, validation, and delivery, shaping data models and workflows using Airflow, Snowflake, and DBT on AWS.

You will own complex technical initiatives, mentor engineers, and collaborate with architecture, DevOps, product, and data teams to deliver reliable, cost-efficient data solutions in a hybrid work environment.