Senior Data Platform Engineer | AI Ops & DevOps Pipelines

MassMutual Ventures

Washington (District of Columbia)

Hybrid

USD 145,000 - 190,000

Full time

14 days+

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Benefits offered by this job

Health benefits

3 well-located offices

Remote work options

Mentorship and growth programs

Wellness program

Job summary

VIA is seeking a Senior Data Platform Engineer to architect and operate secure, scalable data infrastructure. You will build resilient pipelines, optimize performance, and partner with teams to ensure reliable data flows and AI model deployment.

Strong Python, cloud, Kubernetes, and data governance experience are essential. You will collaborate across data professionals, developers, and DevOps to advance observability and reliability while working in a dynamic environment that values innovation

Qualifications

5+ years of experience in a data-driven role.
Bachelor’s or Master’s degree in science, mathematics, engineering, or data-driven field.
A solid foundation in Python for scripting, automation, and tooling across the data platform.
Proven experience architecting scalable relational and NoSQL data schemas.
Expertise in maximizing system performance via query tuning, indexing, and execution plans.
Hands-on with AWS data services (RDS, Aurora, Lambda) or Azure equivalents, plus IaC for provisioning.
Experience with Kubernetes, Docker, and container observability.
Experience building resilient pipelines with Dagster or Airflow.
Strong understanding of how data infra fits into application architecture.
Experience with Git, CI/CD, and infrastructure-as-code; track record deploying production systems.

Responsibilities

Architect, provision, and maintain secure cloud-based data infrastructure with IaC.
Engineer and deploy resilient real-time and batch data pipelines with CI/CD.
Monitor and optimize data infrastructure and pipeline performance.
Collaborate with application teams to troubleshoot data workflows and queries.
Drive data governance, health, integrity, and security across teams.
Operationalize AI models via versioning, containerization, and deployment.