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Remote Data Conversion Developer Jobs in Illinois

Rethink

Senior Database Developer

Chicago, IL · On-site +1

Support schema design, data modeling, and database changes for new features * Collaborate with ... Remote opportunities are available to candidates who reside in the following states: AL, AZ, CT, FL ...

New

Cayuse Holdings, LLC

$28 - $30/hr

AI Engineer Location: Remote Type: Corp to Corp Start Date: ASAP Pay Rate: $28-$30 per hour ... Work with big data platforms and Azure Data Lakes to ensure scalable storage, processing, and ...

Generac

Sales Engineer - Data Centers

Chicago, IL · On-site +1

The Sales Engineer - Data Centers works under the guidance of the Senior Director of Sales - Data ... This position will be remote/hybrid, and the ideal candidate will need to live near a major airport ...

HNI Corporation

Sr. Data Scientist

Chicago, IL · On-site +1

Due to the remote nature of this role and associated employment requirements, the company is unable ... Apply advanced statistical techniques, feature engineering, and algorithm selection to complex ...

HNI Corporation

Sr. Data Scientist

Chicago, IL · On-site +1

Due to the remote nature of this role and associated employment requirements, the company is unable ... Apply advanced statistical techniques, feature engineering, and algorithm selection to complex ...

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Remote Data Conversion Developer information

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

To thrive as a Remote Data Conversion Developer, you need strong programming skills (often in SQL, Python, or ETL tools), data mapping expertise, and an understanding of database structures, typically backed by a degree in computer science or related experience. Familiarity with data conversion platforms such as Informatica, Talend, or SSIS, and certifications in relevant tools or cloud services, are commonly required. Excellent problem-solving, attention to detail, and effective communication are crucial soft skills for collaborating remotely and managing complex data processes. These skills ensure accurate, efficient data transformations and seamless integration across diverse systems in a distributed work environment.

What are some common challenges faced by Remote Data Conversion Developers when working with legacy data systems?

Remote Data Conversion Developers often encounter challenges such as inconsistent data formats, incomplete or corrupted datasets, and undocumented legacy systems. Successfully converting and migrating data requires problem-solving skills to map and validate data accurately, as well as strong communication with business analysts and system owners to clarify requirements and resolve ambiguities. Additionally, thorough testing and quality assurance are essential to ensure data integrity throughout the conversion process.

What is a Remote Data Conversion Developer?

A Remote Data Conversion Developer is a professional who specializes in transforming data from one format or system to another, often working from a remote location. Their main responsibilities include analyzing existing data structures, designing conversion processes, writing scripts or software to automate data migration, and ensuring data integrity during the conversion. They typically work with databases, data warehouses, or legacy systems to facilitate seamless data transitions during system upgrades or platform changes. Strong skills in programming, data analysis, and problem-solving are essential for this role.

What is the difference between Remote Data Conversion Developer vs Data Analyst?

AspectRemote Data Conversion DeveloperData Analyst
Required CredentialsTypically requires programming skills, data conversion tools, and sometimes certifications in data managementRequires analytical skills, proficiency in data visualization, and often a degree in statistics or related fields
Work EnvironmentPrimarily technical, focused on data transformation, ETL processes, and scriptingAnalytical, focused on interpreting data, creating reports, and providing insights
Employer & Industry UsageUsed in IT, data management, and software development sectorsCommon in finance, marketing, healthcare, and business intelligence sectors

The main difference is that Remote Data Conversion Developers focus on transforming and converting data using technical skills, while Data Analysts interpret and analyze data to support decision-making. Both roles may work remotely and require familiarity with data tools, but their core responsibilities differ significantly.

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

GCP Gemini AI Developer

CoSourcing Partners

Chicago, IL • On-site, Remote

Other

Posted 26 days ago

Job description

Job Title: GCP Gemini AI Developer (3-5 Years Experience)
Location: Remote / Hybrid - Chicago preferred
Employment Type: Contract / Full-Time
Reports To: GCP Technical Lead / AI Program Manager
Purpose
The GCP Gemini AI Developer will design, build, and deploy intelligent applications leveraging Google Cloud's Gemini models and Vertex AI platform. This role exists to operationalize advanced GenAI capabilities - including natural language understanding, multimodal reasoning, and generative automation - within scalable, secure, and production-ready cloud environments.
The developer will work hands-on across data engineering, AI model orchestration, and API integration to create AI-driven business solutions that reduce manual effort, enhance decision-making, and unlock measurable value from enterprise data.
Key Performance Outcomes (6-12 Months)OutcomeWhat Success Looks LikeMeasurement1. Gemini-Powered Solutions DeployedDesign, develop, and deploy at least two Gemini-based AI solutions (e.g., document summarization, chat agent, or data extraction automation) using Vertex AI + Gemini APIs.Delivered to production with >90% accuracy and <2s response time.2. Scalable Cloud ArchitectureBuild a modular AI microservices framework using Cloud Run / Cloud Functions with integrated authentication, logging, and monitoring.Reusable components adopted in at least 3 future use cases.3. RAG / Context-Aware WorkflowsImplement Retrieval-Augmented Generation (RAG) pipelines combining Gemini + BigQuery or vector databases for knowledge grounding.Demonstrated 25% reduction in hallucination or response variance.4. Cross-Team EnablementPartner with Data, Automation, and AppDev teams to integrate Gemini AI into existing business workflows (e.g., UiPath, Power Platform, or ServiceNow).Minimum of 2 successful integrations with documented ROI.5. Continuous OptimizationMonitor, retrain, and improve AI models via Vertex AI pipelines and Model Monitoring.Demonstrated 15% performance gain over baseline models.Core Responsibilities
  • Design and deploy Gemini 1.5 Pro/Flash integrations via Vertex AI and Generative AI Studio.
  • Build serverless APIs and backend services for AI workflows using Cloud Run, Functions, or App Engine.
  • Develop data ingestion and preprocessing pipelines using BigQuery, Dataform, and Pub/Sub.
  • Apply prompt engineering and parameter tuning to improve generative model accuracy.
  • Implement RAG pipelines leveraging Vertex Matching Engine or Pinecone.
  • Collaborate with automation and data teams to embed AI into existing business processes.
  • Maintain compliance with security, privacy, and model governance standards.

Technical Environment
Core Google Cloud Services
  • Vertex AI, Generative AI Studio, Gemini API
  • BigQuery, BigQuery ML, Dataform
  • Cloud Run, Cloud Functions, Cloud Storage
  • Pub/Sub, Secret Manager, IAM, Cloud Build

Programming Stack
  • Python or TypeScript (Google Cloud SDKs, google-generativeai, aiplatform)
  • FastAPI / Flask / Node.js
  • LangChain / LlamaIndex for orchestration
  • SQL, Pandas, and Jupyter for data prep

Complementary Tools
  • Terraform (IaC)
  • GitHub / GitLab CI/CD
  • Vertex AI Pipelines & Model Registry
  • Vector DB (Vertex Matching Engine, Pinecone, or Weaviate)

Ideal Profile
  • 3-5 years hands-on GCP development experience with AI/ML exposure
  • Strong working knowledge of Vertex AI, Gemini models, and RAG pipeline design
  • Demonstrated ability to move AI prototypes into production
  • Strong communicator, able to collaborate across automation, data, and cloud teams
  • Curious problem-solver passionate about applied AI innovation

Success Metrics
  • Speed to Delivery: End-to-end deployment within 8-10 weeks per use case
  • Model Effectiveness: >90% accuracy or relevance rating from business stakeholders
  • Scalability: Framework reused for 3 additional AI initiatives
  • Business Impact: 25%+ improvement in productivity or efficiency from deployed use cases

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