Data Engineer

2 weeks ago

Chennai, Tamil Nadu, India iAgami Full time ₹ 20,00,000 - ₹ 25,00,000 per year

We're Hiring: Data Engineer – Platform & Distributed Systems

Are you a builder at heart who loves working at the intersection of data engineering, cloud infrastructure, and ML platforms?

We're looking for a Data Engineer to join our team and help scale our JupyterLab-based Notebook platform, enabling data scientists and engineers across cloud and on-premise environments. This role is ideal for someone who's passionate about developing powerful tools, enabling distributed processing, and ensuring secure, scalable infrastructure for advanced analytics and machine learning.

What You'll Work On:

Enhance and maintain our custom Notebook platform (JupyterLab) for data science and ML workflows.

Integrate diverse data sources: BigQuery, Hive, Teradata, Hadoop, Spark, and more.

Enable distributed computing with Spark, PySpark, and Dataproc.

Implement features for code execution, visualization, scheduling (Airflow), and collaboration.

Ensure platform security with SSO, RBAC, and compliance to governance standards.

Support ML workflows, including model training, tracking (MLflow), and deployment pipelines.

Monitor and optimize performance across cloud-native environments (GKE, Kubernetes, OpenShift).

Collaborate with cross-functional teams: data scientists, engineers, and product managers.

Contribute to onboarding documentation, best practices, and user guides.

What You Bring:

Proficiency in Python for backend and data engineering.

Hands-on experience with JupyterLab/Notebook customization and extensions.

Expertise in distributed data processing: Spark, PySpark, Dataproc.

Solid experience with cloud platforms (preferably GCP), Kubernetes, and Docker.

Strong understanding of integrating structured and unstructured data sources (BigQuery, Hive, Teradata, etc.).

Experience with CI/CD, DevOps, and observability tools.

Solid grasp of security best practices – SSO, RBAC, secret management.

Familiarity with ML lifecycle tools like MLflow, and ML model deployment.

Strong debugging, communication, and stakeholder collaboration skills.

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