Data & AI Platform Engineer

Armanino LLP•Chicago, IL

1d•Hybrid

About The Position

At Armanino, you determine your career path. This means it's possible to pursue challenges you are passionate about, in industries you care about. Armanino is proud to be Among the top 20 Largest Accounting and Consulting Firms in the Nation and one of the Best Places to Work. We have a community of resources that are ready and willing to support your ideas, build your skills and expand your professional network. We want you to integrate all aspects of your life with your career. At Armanino, we know you don’t check-out of life when you check-in at work. That’s why we’ve created a unique work environment where your passions, work, and family & friends can overlap. We want to help you achieve growth by giving you access to a network of smart and supportive people, willing to listen to your ideas. We deliver reporting, analytics, warehousing, and targeted ML/AI/GenAI solutions across Microsoft Cloud (Azure, M365, Dynamics, Power Platform) and hybrid/on-prem environments. We partner with application teams and systems engineering to run secure, reliable, and well-governed platforms. This is an early-career engineering role focused on building, operating, and improving cloud data/analytics platforms (e.g., Microsoft Fabric, Snowflake, Databricks) and supporting BI delivery (e.g., Power BI, Tableau). You’ll contribute to platform reliability, CI/CD, environment setup, and operational runbooks—while learning best practices in security, governance, and deployment automation from senior engineers.

Requirements

Minimum 2 years’ experience in a data engineering, platform engineering, analytics engineering, or cloud operations role (internships/co-ops count).
Comfort with one or more of the following areas (not all): A cloud data platform: Snowflake (preferred) or Microsoft Fabric or Databricks A BI platform: Power BI or Tableau CI/CD concepts (Git, branching/PRs, basic pipelines)
Basic scripting capability in Python or PowerShell (or willingness to learn quickly).
Strong troubleshooting mindset: can break problems down, gather evidence, and communicate clearly.
Ability to document what you learned (runbooks, checklists, short “how-to” guides).
Exposure to cloud concepts: identity/access, resource organization, logging/monitoring.
Familiarity with SQL and performance basics (indexes/partitioning concepts, query plans at a high level).
Understanding of data governance concepts (PII, RLS/CLS, environment separation).
Ability to work onsite in any of our office or at a client site up to 50% of the time.

Nice To Haves

Any relevant certs (Azure fundamentals, Snowflake, Databricks, etc)
Demonstrated experience with AI/ML/GenAI enablement (model lifecycle, AI Search, Azure OpenAI integration, or MLOps).

Responsibilities

Platform Operations & Enablement (Foundational) Support day-to-day operations for cloud data platforms (workspace/project setup, access requests, basic configuration, troubleshooting).
Assist with platform hygiene: organizing environments, documenting standards, and improving repeatability.
Help implement and maintain guardrails (naming/tagging conventions, access patterns, basic cost awareness).
DevOps & Deployment Support (Guided) Contribute to CI/CD workflows for data and analytics assets (pipelines, jobs, semantic models, reports), under mentorship.
Help maintain reusable templates/checklists for deployments (approvals, promotion steps, rollback notes, release documentation).
Data/Analytics Tooling Support Assist teams with onboarding and “how-to” enablement across a core platform (Fabric or Snowflake or Databricks) plus a BI tool (Power BI or Tableau).
Support basic performance triage (query/job failures, refresh issues, common workspace capacity constraints) and escalate effectively when needed.
Reliability, Monitoring, and Runbooks Help build and maintain runbooks (standard operating procedures, known issues, quick fixes, escalation paths).
Participate in incident response support (triage, notes, follow-ups) and contribute to preventative improvements.
Security & Governance (Learning-to-Doing) Follow and reinforce least-privilege access practices and secure secret handling (e.g., Key Vault/secret scopes where applicable).
Assist with data governance basics (PII handling expectations, row-level access concepts, environment separation).