Senior Data Engineer: Ingestion & Cloud Connectors (Hybrid)

The Walt Disney Company; Buenos Aires; Híbrido; ARS 139.086.000 - 200.903.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive salary package

Value-based recognition awards

Referral bonus

Career development programs

Hybrid work model

Wellbeing program

Global tech communities

Descripción de la vacante

Endava is seeking a Data Engineer to design, build, and optimize scalable data pipelines and architectures. You will implement Airflow-based workflows, ensure data quality with DBT and SQL, and collaborate across cloud-native data platforms (Snowflake, AWS) to deliver reliable, production-grade data solutions.

The role emphasizes ownership of end-to-end data engineering initiatives, mentoring teammates, and contributing to best practices in a fast-paced, hybrid work environment.

Formación

• 5+ years of professional experience in data engineering or a related software engineering role.
• 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 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

DBT

Snowflake

AWS

CI/CD

Terraform

Herramientas

Apache Airflow

Snowflake

DBT

AWS

Descripción del empleo

Endava is seeking a Data Engineer to design, build, and optimize scalable data pipelines and architectures. You will implement Airflow-based workflows, ensure data quality with DBT and SQL, and collaborate across cloud-native data platforms (Snowflake, AWS) to deliver reliable, production-grade data solutions.

The role emphasizes ownership of end-to-end data engineering initiatives, mentoring teammates, and contributing to best practices in a fast-paced, hybrid work environment.