Senior Data & AI Platform Engineer

Armanino•San Ramon, CA

About The Position

This is a senior engineering role. You will architect, operate, and continuously improve cloud data and analytics platforms (e.g., Microsoft Fabric, Snowflake, Databricks) and own BI delivery infrastructure (e.g., Power BI, Tableau). You set the standards for platform reliability, CI/CD, environment design, security, and governance, and you carry those standards through to production. You work independently, make architecture calls, and own the outcome.

Requirements

Minimum 6 years in data engineering, platform engineering, analytics engineering, or cloud operations, with a track record of owning platforms in production.
Minimum 2 years of representative accounting experience, whether implementing accounting systems or working in direct accounting roles.
Deep, production-grade command of a cloud data platform: Snowflake (preferred), Microsoft Fabric, or Databricks, with working fluency across the others.
Strong command of a BI platform: Power BI or Tableau, including data modeling, security, and refresh architecture.
CI/CD ownership: Git, branching and PR discipline, and building pipelines, not just running them.
Solid scripting in Python or PowerShell.
Advanced troubleshooting: you break down ambiguous problems, gather evidence, decide, and communicate clearly to technical and non-technical audiences.
Strong SQL and performance skills: indexing and partitioning strategy, query plans, and capacity tuning.
Working command of cloud fundamentals: identity and access, resource organization, logging, and monitoring.
Clear technical writing: runbooks, standards, and architecture documentation others can follow.

Nice To Haves

Relevant certifications (Azure, Snowflake, Databricks, etc.) are a plus.
Hands-on AI/ML/GenAI enablement experience (model lifecycle, AI Search, Azure OpenAI integration, or MLOps) is a strong plus.

Responsibilities

Own the design and day-to-day operation of cloud data platforms: workspace and project architecture, access models, capacity planning, configuration standards, and troubleshooting.
Define and enforce platform standards: naming and tagging conventions, access patterns, cost management, and environment separation.
Drive platform maturity: identify structural gaps, propose the fix, and implement it without waiting to be told.
Design, build, and own CI/CD workflows for data and analytics assets (pipelines, jobs, semantic models, reports), including branching strategy, promotion, approvals, and rollback.
Establish reusable templates, checklists, and release documentation that the rest of the team deploys against.
Set the deployment bar and hold the team to it through review and automation.
Serve as the technical authority across a core platform (Fabric, Snowflake, or Databricks) plus a BI tool (Power BI or Tableau), and provide working depth across the others.
Lead onboarding and enablement for engineering and client teams: patterns, guardrails, and reusable solutions rather than one-off answers.
Own performance: diagnose and resolve query, job, refresh, and capacity issues, and design the platform so they stop recurring.
Own platform reliability. Design monitoring and alerting, set SLOs where they matter, and reduce operational toil.
Build and maintain the runbook library (standard operating procedures, known issues, escalation paths) and keep it current.
Lead incident response: drive triage, own the resolution, run the post-incident review, and land the preventative fix.
Define and enforce least-privilege access and secure secret handling (e.g., Key Vault, secret scopes) across environments.
Own data governance in practice: PII handling, row- and column-level security, and environment separation, aligned to firm and client policy.
Review the work of other engineers for security and governance before it reaches clients.