Senior Data Platform Engineer

Digi-Key Electronics; India; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Digi-Key Electronics in India is looking for a Dataiku Platform Administrator to manage end-to-end administration of Dataiku DSS environments, troubleshoot performance issues, and collaborate closely with data scientists to productionize machine learning workflows.

The ideal candidate will have 7+ years of platform engineering experience, with extensive knowledge in Dataiku administration and Linux system administration. This role offers an exciting opportunity to work with enterprise-scale solutions and mentor junior engineers.

Qualifications

• 7+ years of experience in platform engineering or DevOps.
• 3+ years of hands-on Dataiku administration experience.
• Strong experience with Linux, Python, CI/CD pipelines, and cloud platforms.

Responsibilities

• Own administration of Dataiku DSS environments.
• Design and support model deployment pipelines.
• Administer Dataiku on Linux-based environments.

Skills

Dataiku administration

Linux system administration

Python scripting

CI/CD pipelines

Cloud platforms

ModelOps/MLOps implementation

Troubleshooting

Analytical skills

Problem-solving skills

Education

Bachelor's degree in Computer Science or related field

Tools

Dataiku

Kubernetes

Snowflake

Ansible

Terraform

Job description

Dataiku Platform Administration

• Own end-to-end administration of Dataiku DSS environments (installation, upgrades, patching, backups, restores).
• Manage hub-and-spoke or multi-instance Dataiku architectures.
• Troubleshoot and resolve performance, infrastructure, and platform stability issues.
• Apply security best practices including user access control, secrets management, and vulnerability remediation.
• Plan and execute capacity management, scaling, and disaster recovery strategies as needed.

ModelOps / MLOps Enablement

• Design and support model deployment pipelines using Dataiku automation, APIs, and CI/CD tools.
• Enable model versioning, promotion, rollback, and lifecycle management across environments.
• Implement model monitoring (performance drift, data drift, operational health).
• Partner with data scientists to productionize ML workflows using best‑practice ModelOps patterns.
• Define and document standard operating procedures (SOPs) for model deployment and operations.

Cloud, Infrastructure & Automation

• Administer Dataiku on Linux‑based environments across cloud platforms.
• Work with containers and Kubernetes for scalable and resilient deployments.
• Build automation using Python, Bash, Ansible, or Terraform to reduce manual operations.
• Integrate Dataiku with enterprise tools such as Git, CI/CD pipelines, monitoring (Grafana/Prometheus), and ServiceNow.
• Administer and support Dataiku integrations with enterprise databases – preferably Snowflake.
• Troubleshoot query performance, concurrency, and cost‑related issues arising from Dataiku workloads on Snowflake.

Collaboration & Support

• Act as the primary platform contact for Dataiku.
• Provide support and participate in incident response and root‑cause analysis.
• Mentor junior engineers to enable L1/L2 support and contribute to internal platform standards.
• Work closely with vendors during transition phases and drive in‑house ownership of the platform.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (16 years of formal education).
• 7+ years of experience in platform engineering, data platform administration, or DevOps.
• 3+ years of hands‑on Dataiku administration experience.
• Strong experience with Linux system administration, Python scripting, CI/CD pipelines and Git‑based workflows, cloud platforms (Azure, AWS, or GCP), implementing ModelOps/MLOps concepts, including model deployment and monitoring, familiarity with Kubernetes and containerised workloads, strong troubleshooting, analytical, and problem‑solving skills.

Preferred / Nice‑to‑Have Skills

• Dataiku certifications (Core Designer, Advanced Designer, ML Practitioner).
• Experience supporting enterprise‑scale ML platforms.
• Knowledge of monitoring and observability tools (Grafana, Prometheus).
• Exposure to IaC tools such as Terraform or Ansible.
• Experience transitioning platforms from vendor‑managed to in‑house support.