top of page
c_smart_people_logo_INVERSE.png

Data Engineer

Contract

​

with strong hands-on expertise in dbt, Argo
Workflows, and Kubernetes-based data platforms. The ideal candidate will design, build, and
operate scalable, reliable data pipelines that power analytics, reporting, and downstream data
consumers in a cloud-native environment.

Requirements

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 running 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.
 


Benefits

Required Skills & Qualifications
 
 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 (e.g., AWS S3 - Redshift,
Snowflake, or similar).
 Exposure to large data sets.
 Strong hands-on expertise with DBT (Data Build Tool) – ETL Transformation - for
data transformation, testing, and modeling.

 Proven experience orchestrating workflows using Argo Workflows (orchestration
for scheduling – can be cross trained from airflow)
 Solid working knowledge of Kubernetes, including pod execution, resource
management, and debugging.
 Familiarity with CI/CD pipelines, Git-based version control, and infrastructure-as-
code concepts.
 Strong understanding of data warehousing principles, dimensional modeling, and
ELT architectures.
 Excellent problem-solving skills, attention to detail, and ownership mindset.
 Strong communication skills and ability to collaborate across cross-functional
teams.
 Experience running dbt on Kubernetes using Argo or similar orchestrators.
 Exposure to cloud-native observability tools (logging, monitoring, alerting).
 Experience with modern data warehouses (Snowflake, BigQuery, Redshift).
 Knowledge of data governance, access control, and metadata management.
 Familiarity with visualization tools such as Tableau, Power BI, or Looker.
 Cloud certifications (AWS, GCP, or Kubernetes) are a plus.

bottom of page