Data, Analytics & Cloud Engineering Consultant

Kenway Consulting•Chicago, IL

6d•$120,000 - $140,000•Onsite

About The Position

Kenway Consulting is seeking a Data, Analytics & Cloud Engineering Consultant. This is a client-facing advisory role focused on solving critical technology and business problems using data, analytics, and cloud engineering disciplines. Consultants will build solutions on modern cloud data platforms and analyze data to provide insights. The role is designed for early-career professionals who will work with senior engineers and architects, supporting their growth into well-rounded advisors. The ideal candidate has a passion for data-driven decision-making, strong communication skills, and professionalism. They should enjoy collaborative work with diverse clients and industries, and be eager to take ownership of their tasks.

Requirements

Qualification(s) in data analytics, information management, computer science, or equivalent practical experience
1–3 years of experience (including internships / co-ops) building, implementing, and maintaining data solutions on Modern Data Platforms
Cloud architecture and engineering, with a focus on Microsoft Azure — a working understanding of core Azure data services (e.g., storage, compute, Synapse, OneLake) and how they combine to support a data platform, plus hands-on exposure to cloud engineering practices such as provisioning and configuring resources, managing access and secrets (RBAC, managed identities, Key Vault), and building with cost and security in mind
Hands-on exposure to modern data platforms, specifically Databricks and/or Microsoft Fabric — building and running data pipelines and developing in notebooks
Proficiency in Python and SQL, with a demonstrated ability to perform real analysis — not just query data — including SQL performance and tuning fundamentals
Analytical problem-solving — the ability to explore data, investigate anomalies, reason from symptoms to root cause, and translate findings into a clear explanation of what the business is seeing
Exposure to AI in the modern data and analytics stack — familiarity with how AI is reshaping data work, such as AI-assisted and conversational analytics (e.g., Databricks Genie, Microsoft Fabric Copilot / Data Agents), applying large language models to data problems, and using AI coding assistants to work more effectively, with the curiosity to keep pace as these tools evolve
An understanding of how CI/CD and DevOps should work in the data space, including: Git-based version control of pipelines, notebooks, and data models; Promoting code across development, test, and production environments; Automated testing, logging, and alerting for data pipelines
Familiarity with relevant tooling (e.g., Azure DevOps, GitHub Actions, dbt, Databricks Asset Bundles / Repos, or Fabric deployment pipelines)
Working knowledge of ELT/ETL approaches, including batch and, ideally, real-time or event-driven processing
Familiarity with Data Lake, Data Lakehouse, and Data Warehouse concepts
Familiarity with data modeling and data transformation (e.g., dbt)
Exposure to Agile ways of working and delivery tooling such as JIRA or Azure DevOps Boards
Business acumen — able to connect technical work to the business problem it solves, and to take ownership of defined work and deliver value with limited oversight

Nice To Haves

Entrepreneurship: Passion to fuel growth by helping clients, peers, the organization, and yourself, with a continuous hunger to learn.
Tenacity: Ability to drive through ambiguity.
Professional Presence: Clear, concise, and articulate when using written and verbal channels.
Communication: Adept at communicating quickly and effectively, whether the message is good or bad.
Aptitude: You climb steep learning curves quickly.
Integrity: Comfortable doing what is right for the project even when doing so may not be the most favorable path forward.
Growth Mindset: Proactively develop core skills independently and with the help of others.

Responsibilities

Build and maintain scalable, performant, and cost-effective data pipelines, notebooks, and data products (including Power BI reports) on modern cloud data platforms, under the direction of senior engineers and architects
Analyze data to investigate problems, identify root causes, and interpret what the business is seeing — then communicate those findings clearly
Write clean, well-structured Python and SQL to move, model, transform, and analyze data
Apply software-delivery discipline to data assets — version control, testing, and CI/CD-based deployment across environments
Take ownership of defined technical tasks and deliver them with increasing independence
Support the proposals, demos, and collateral leveraged in business development pursuits