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Remote Ai Data Engineer Jobs in Illinois (NOW HIRING)

PeopleCaddie

Data Engineer - Manager

Chicago, IL · Remote

$80 - $85/hr

Fully Remote Rate: $80-$85/hour (C2C) Role Overview The Senior Data Engineer plays a critical role ... Collaborate with architects, data scientists, AI engineers, and analysts to support advanced ...

3B Staffing LLC

Data Engineer

Mount Sterling, IL · Remote

$104.30K - $125.30K/yr

DB2 * watsonX.ai * Supply Chain Date Candidate Name C-Phone C-Email Data Engineer Years Snowflake ... Automate data wrangling from a wide variety of sources (flat files, remote servers, databases ...

Voluble Systems LLC

Data Engineer

Chicago, IL · Remote

$118K - $141.80K/yr

Location: 100% Remote Duration: Contract - 4 month project No OT: Only seeking candidates on a ... Collaborates with architects, data scientists, AI engineers, and analysts to manage data as an ...

Accordion USA

AI Data Specialist

Chicago, IL · On-site +1

You'll work directly alongside AI engineers and product managers to scope data requirements, diagnose quality issues, and build the data foundations that AI systems depend on. Client-facing moments ...

PAR Technology

Sr. Data Engineer

Chicago, IL · On-site +1

$130K - $144K/yr

Remote, United States Reports To: Engineering Manager What We're Looking For: Requirements ... AI/ML Integration : Ability to integrate AI/ML models into data pipelines * LLM Tools: E xperience ...

Inspire11

Data Engineer

Chicago, IL · On-site +1

$118K - $141.80K/yr

Data Engineer - Inspire11 Elevens, as we call ourselves here, are curiously smart, creative, and ... Develop AI-first, cloud-native data solutions using Microsoft Fabric, Databricks, Snowflake, and ...

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Remote Ai Data Engineer information

What are the key skills and qualifications needed to thrive as a Remote AI Data Engineer, and why are they important?

To thrive as a Remote AI Data Engineer, you need strong programming skills (Python, SQL), a solid understanding of data structures, machine learning principles, and typically a degree in computer science or related fields. Familiarity with big data platforms (such as Hadoop or Spark), cloud services (AWS, GCP, or Azure), and experience with AI/ML frameworks like TensorFlow or PyTorch are commonly required. Excellent problem-solving, communication, and self-motivation skills help you collaborate effectively and manage projects independently in a remote setting. These skills and qualities ensure robust AI data pipelines, effective model deployment, and seamless teamwork across distributed environments.

What are some common challenges faced by Remote AI Data Engineers, and how can they be addressed?

Remote AI Data Engineers often encounter challenges such as coordinating with cross-functional teams across different time zones, ensuring data security when accessing sensitive datasets remotely, and maintaining effective communication for project updates. To address these, it's important to establish clear protocols for data sharing, leverage collaboration tools (like Slack or Jira), and schedule regular check-ins to align with team goals. Adopting strong version control practices and automated testing can also help streamline workflows and minimize errors in a distributed environment.

What is a Remote AI Data Engineer?

A Remote AI Data Engineer is a professional who designs, builds, and maintains data pipelines and infrastructure to support artificial intelligence (AI) and machine learning (ML) projects, all while working from a remote location. They are responsible for collecting, cleaning, transforming, and storing large datasets, ensuring data quality and accessibility for AI applications. These engineers collaborate with data scientists, software engineers, and stakeholders to deliver data solutions that power intelligent systems, often leveraging cloud technologies and distributed computing. Their work enables organizations to harness data for predictive analytics, automation, and decision-making—without being tied to a physical office.

What is the difference between Remote Ai Data Engineer vs Data Scientist?

AspectRemote Ai Data EngineerData Scientist
Required CredentialsBachelor's in CS, Data Engineering, or related; experience with cloud platformsBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentData pipelines, cloud infrastructure, codingData analysis, statistical modeling, visualization
Employer & Industry UsageTech companies, AI firms, startupsResearch institutions, tech companies, finance
Common Search & ComparisonYesYes

Remote Ai Data Engineers focus on building and maintaining data pipelines and infrastructure for AI applications, requiring skills in data engineering and cloud platforms. Data Scientists analyze data, develop models, and generate insights. While both roles work with data, Data Engineers prepare the data environment, whereas Data Scientists interpret and model the data. They often collaborate but serve different functions in AI and data projects.

What are the most commonly searched types of Ai Data Engineer jobs in Illinois? The most popular types of Ai Data Engineer jobs in Illinois are:
What are popular job titles related to Remote Ai Data Engineer jobs in Illinois? For Remote Ai Data Engineer jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Remote Ai Data Engineer jobs in Illinois look for? The top searched job categories for Remote Ai Data Engineer jobs in Illinois are:
What cities in Illinois are hiring for Remote Ai Data Engineer jobs? Cities in Illinois with the most Remote Ai Data Engineer job openings:
Remote Ai Data Engineer jobs near you
Data Engineer - Manager

Data Engineer - Manager

PeopleCaddie

Chicago, IL • Remote

$80 - $85/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Job Description:
Title: Senior Data Engineer - Contract
Client: Large Public Accounting Firm
Engagement: 6-12 Months+ Contract
Work Model: Fully Remote
Rate: $80-$85/hour (C2C)
Role Overview
The Senior Data Engineer plays a critical role in advancing a modern, enterprise-wide data strategy focused on enabling data-driven decision-making, advanced analytics, and AI-powered insights. This role supports a centralized Data & Analytics function aligned to core technology pillars including Application Modernization, AI, and Data.
You will be responsible for designing, building, optimizing, and maintaining scalable data platforms and pipelines that support analytics, reporting, AI/ML, and operational intelligence across the organization. This is a senior, hands-on engineering role requiring deep technical expertise, strong collaboration skills, and the ability to translate complex data challenges into reliable, high-value solutions.
Key Responsibilities
  1. Design, develop, and maintain scalable and resilient data pipelines for ingesting, transforming, and delivering data from diverse internal and external sources.
  2. Integrate data across databases, data warehouses, APIs, and third-party platforms while ensuring data accuracy, consistency, and integrity.
  3. Apply data cleansing, validation, aggregation, enrichment, and transformation techniques to prepare analytics-ready datasets.
  4. Optimize data pipelines and processing workflows for performance, scalability, reliability, and cost efficiency.
  5. Monitor and tune data systems; identify performance bottlenecks and implement indexing, caching, and optimization strategies.
  6. Embed data quality checks, validation rules, and governance controls directly within data pipelines.
  7. Collaborate with architects, data scientists, AI engineers, and analysts to support advanced analytics, business intelligence, and AI/ML use cases.
  8. Take ownership and accountability for maximizing the value of enterprise data assets used for insights, automation, and decision support.
  9. Clearly communicate complex technical concepts to both technical and non-technical stakeholders, including senior leadership.
Required Experience & Qualifications
Bachelor's degree in Computer Science, Data Science, Software Engineering, Information Systems, or a related quantitative field.
8+ years of experience in data engineering, including:
  1. Data modeling and architecture
  2. ETL / ELT and data integration
  3. Data warehousing and analytics platforms
  4. Data quality, master data management, and governance
  5. Business intelligence and advanced analytics (predictive and prescriptive)
Strong programming experience with Python, SQL, Java, and/or C#.
Hands-on experience with modern data platforms and tools, including:
  1. Microsoft Azure technologies (SQL Server IaaS/PaaS, Synapse, Cosmos DB, Azure Data Factory, Databricks, HDInsight, Fabric, Power BI)
  2. Informatica Cloud (CIH, DIH, CDGC, Master Data Management, Data Quality)
  3. Snowflake and other leading cloud data technologies

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