Data Engineer – Cloud & Kubernetes

About the role

We are looking for a Data Engineer to design, develop, and maintain scalable ELT pipelines using dbt, orchestrate workflows with Argo on Kubernetes, and collaborate with cross‑functional teams to deliver reliable, cloud‑native data solutions.

Key responsibilities

• Design, develop, and maintain scalable ELT pipelines using dbt for data transformation and modeling.
• Orchestrate and manage data workflows using Argo Workflows in a Kubernetes environment.
• Build and operate cloud‑native data pipelines on Kubernetes, ensuring reliability, scalability, and observability.
• Collaborate with analytics, data science, and business teams to translate data requirements into well‑modeled datasets.
• Implement data quality checks, testing, and monitoring within dbt and orchestration layers.
• Optimize performance of data transformations, queries, and workflow executions.
• Ensure data security, governance, and compliance standards are followed.
• Participate in code reviews, documentation, and knowledge sharing across the team.
• Troubleshoot and resolve data pipeline failures, performance bottlenecks, and infrastructure issues.
• Support CI/CD practices for data pipelines and dbt projects.

Required profile

• 3–8 years of experience in data engineering or related roles.
• Advanced SQL skills and proficiency in Python and/or Shell scripting.
• Experience with cloud data platforms and storage such as AWS S3, Redshift, Snowflake, or similar.
• Strong hands‑on expertise with dbt for transformation, testing, and modeling.
• Proven experience orchestrating workflows using Argo Workflows (or similar tools like Airflow).
• Solid working knowledge of Kubernetes, including pod execution, resource management, and debugging.
• Familiarity with CI/CD pipelines, Git version control, and infrastructure‑as‑code concepts.
• Understanding of data warehousing principles, dimensional modeling, and ELT architectures.
• Excellent problem‑solving skills, attention to detail, and an ownership mindset.
• Strong communication skills and ability to collaborate across cross‑functional teams.

Required skills

• SQL
• Python
• Shell scripting
• dbt (Data Build Tool)
• Argo Workflows
• Kubernetes
• AWS (S3, Redshift)
• Snowflake
• BigQuery
• CI/CD pipelines
• Git
• Infrastructure as Code
• Data warehousing & dimensional modeling
• ELT architectures
• Observability tools (logging, monitoring, alerting)
• Tableau / Power BI / Looker