Senior Associate - AI Data Engineer

Andersen Tax • Chicago, IL

40d

About The Position

We're looking for an experienced Data Engineer to build the data and feature engineering pipelines that power machine learning and GenAI workloads. Data engineers at Andersen Consulting interact with client technical teams, internal engagement leaders, and internal product managers to understand client data landscapes. Their core responsibility is implementing modern data pipelines to support client use cases. This is an individual contributor role which does not require people management. You'll work within the new Andersen Consulting Global AI practice which is accountable for delivering value to clients through machine learning and GenAI. The team does this by using modern techniques, tooling, and behaviors to deliver value as quickly as possible. Since the AI landscape is moving fast, you'll be asked to learn new things quickly and accomplish a wide range of tasks, all focused on delivering robust data and feature engineering pipelines to clients with exceptional quality. Your initial focus will be on becoming proficient using the Palantir Foundry platform. Andersen Consulting is partnering with Palantir to build and deploy Foundry solutions to Andersen Consulting clients. This means that in addition to working on client engagements, you'll have the opportunity to become an expert in one of the most powerful AI platforms on the market. Longer term, you will be tasked with building bespoke data and feature engineering pipelines within client infrastructure which may include on-premises software, cloud-hosted software, or cloud-native services. We understand that narrow and well-defined job responsibilities are good for some people. If you are one of those people, this role may not be right for you. We expect you to: Come ready to do the best work of your life and influence your team to do the same Deliver data and feature engineering pipelines with exceptional quality and be ready to be held to that standard Work directly with client technical teams to explore source data and help define required transformations Work closely with internal product teams to implement feature engineering where required Set the standard for disciplined data engineering (e.g. automated testing, continuous integration and deployment, code reviews and writing high quality, well documented code) Champion good agile practices that provide a foundation for iterative product development

Requirements

• A technical background in computer science, data science, machine learning, artificial intelligence, statistics or other quantitative and computational science
• A compelling track record of designing and deploying large scale data engineering solutions, which deliver tangible, ongoing value (and ability to show work)
• Professional experience writing high-quality Python code
• Professional experience working with relational and non-relational database technologies with expert level knowledge of SQL
• Professional experience working in PySpark, Polars, Typescript, and Java
• Professional experience working within teams to collectively build and deploy high quality code using the tools of the trade (e.g. Git, Slack)
• Professional experience with cloud native architectures, services, and tools

Nice To Haves

• Ability to context-switch, to provide support to dispersed teams which may need an "expert hacker" to unblock an especially challenging technical obstacle
• An 'engineering' mindset, willing to make rapid, pragmatic decisions to improve performance, accelerate progress or magnify impact; recognizing that the 'good' is not the enemy of the 'perfect'
• Strong willingness to learn modern tools in the GenAI ecosystem (e.g., vector databases, feature stores, prompt engineering platforms, LLM orchestration libraries, model-serving frameworks, AI observability tools).
• Exposure to machine learning operations and large language model (LLM) operations tools and practices

Responsibilities

• Deliver data and feature engineering pipelines with exceptional quality and be ready to be held to that standard
• Work directly with client technical teams to explore source data and help define required transformations
• Work closely with internal product teams to implement feature engineering where required
• Set the standard for disciplined data engineering (e.g. automated testing, continuous integration and deployment, code reviews and writing high quality, well documented code)
• Champion good agile practices that provide a foundation for iterative product development

Benefits

• Employees (and their families) are eligible for medical, dental, vision, and basic life insurance coverage.
• Employees may enroll in the firm's 401(k) plan upon hire.
• We offer 160 hours of paid time off annually, along with twelve paid holidays each calendar year.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Professional, Scientific, and Technical Services
• Education Level: No Education Listed