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About This Role
Are you looking for an opportunity to do challenging, creative work that makes a real impact? Would you like to be part of a company that respects individual contributions and thrives on collaboration? Do you love to win? Then consider joining Wight \& Company. We're a nationally recognized, award\-winning architecture, engineering, and construction firm known for a dynamic company culture that fosters innovation and challenges the status quo, and we're looking for a talented professional to join our team.
Wight \& Company is seeking an AI Solutions \& Data Engineer to help turn artificial intelligence and organizational data into practical solutions for our teams, clients, and projects. This hybrid role will work across architecture, engineering, construction, and business operations to identify valuable AI opportunities, develop AI agents and automated workflows, connect information from multiple systems, and help employees confidently apply AI in their work. The ideal candidate combines technical ability with curiosity, creativity, and strong communication skills. This person should be comfortable building solutions directly, facilitating conversations with teams, translating business challenges into technical approaches, and teaching others how to use new tools effectively. The AI Solutions \& Data Engineer will report to the Chief Innovation Officer and collaborate with technology, BIM, operations, and departmental leaders across the company.
ESSENTIAL RESPONSIBILITIES
Develop AI Solutions and Agents
- Design, build, test, and maintain AI agents that support project delivery and business operations.
- Develop solutions using large language models, retrieval\-augmented generation, workflow automation, APIs, and related technologies.
- Connect AI tools with internal applications, document repositories, databases, and third\-party platforms.
- Create prototypes and pilot programs that allow teams to evaluate new ideas quickly.
- Establish methods for testing solution accuracy, reliability, security, and business value.
- Help move successful prototypes into maintainable, production\-ready solutions.
- Monitor the performance and adoption of deployed AI solutions and improve them over time.
Build and Manage Data Solutions
- Gather, clean, organize, and combine data from multiple business and project systems.
- Develop and maintain data pipelines, integrations, structured datasets, and reusable data services.
- Help establish reliable connections between project, financial, operational, and knowledge\-management platforms.
- Improve the accessibility and usefulness of organizational data while maintaining appropriate security and permissions.
- Collaborate with system owners to improve data quality, consistency, documentation, and governance.
- Support dashboards, reporting, predictive analytics, and AI applications that depend on reliable data.
- Identify opportunities to reduce duplicate data entry and improve the flow of information across the organization.
Identify and Prioritize AI Opportunities
- Meet with teams across the company to understand their work, challenges, and opportunities.
- Facilitate workshops and brainstorming sessions focused on meaningful business problems rather than technology alone.
- Translate business needs into clearly defined AI, automation, or data initiatives.
- Evaluate potential solutions based on value, feasibility, risk, scalability, and alignment with company priorities.
- Help departments develop practical roadmaps for adopting AI and improving their use of data.
- Stay informed about emerging AI capabilities and evaluate where they may create value for the company or its clients.
Train and Support Teams
- Develop and deliver practical AI training for employees with varying levels of technical experience.
- Teach teams how to use approved AI tools effectively, securely, and responsibly.
- Create guides, demonstrations, templates, prompt libraries, and other reusable learning resources.
- Coach individuals and teams as they incorporate AI into existing workflows.
- Support a network of internal AI champions who can help expand adoption across the organization.
- Share successful use cases and lessons learned to encourage continuous improvement.
Support Responsible AI and Data Governance
- Help establish technical standards for AI development, data integration, documentation, testing, and deployment.
- Apply appropriate security, privacy, intellectual\-property, and data\-governance requirements to AI solutions.
- Work with technology, legal, risk, and business leaders to evaluate proposed tools and use cases.
- Maintain clear documentation of data sources, integrations, models, prompts, permissions, dependencies, and system behavior.
- Promote responsible human oversight and clearly communicate the limitations of AI\-generated results.
BACKGROUND \& EXPERIENCE EXPECTATIONS
- Bachelor's degree in computer science, data engineering, information systems, software engineering, or a related field—or equivalent professional experience.
- Experience developing AI\-enabled applications, software integrations, data solutions, or workflow automations.
- Proficiency in Python, SQL, APIs, and common data\-processing methods.
- Experience working with structured and unstructured data from multiple sources.
- Understanding of modern generative AI concepts, including large language models, embeddings, vector search, tool use, and retrieval\-augmented generation.
- Experience developing with cloud platforms, databases, automation tools, or enterprise application ecosystems.
- Ability to translate loosely defined business challenges into practical technical solutions.
- Strong written, verbal, presentation, and facilitation skills.
- Ability to explain technical concepts clearly to nontechnical audiences.
- A collaborative mindset and an interest in working directly with employees across many disciplines.
- Sound judgment regarding data privacy, cybersecurity, intellectual property, and responsible AI use.
- Ability to balance experimentation and speed with reliability, documentation, and long\-term maintainability.
PREFERRED QUALIFICATIONS
- Experience with Microsoft Azure, Microsoft 365, Copilot Studio, Power Platform, Fabric, or related Microsoft technologies.
- Experience with AI platforms and APIs such as Azure OpenAI, OpenAI, Anthropic, or similar services.
- Experience with ETL/ELT processes, data warehouses, lakehouses, semantic models, or business\-intelligence platforms.
- Familiarity with agent frameworks, orchestration tools, vector databases, or enterprise search technologies.
- Experience with application development, source control, testing, deployment, and DevOps practices.
- Experience delivering technical training, facilitating workshops, or supporting organizational change.
- Familiarity with architecture, engineering, construction, professional services, or project\-based organizations.
- Experience integrating enterprise systems such as Microsoft 365, Autodesk Construction Cloud, Procore, Bentley, Unanet, CMiC, Egnyte, or similar platforms.
- Familiarity with BIM, digital project delivery, estimating, construction management, or design technology is beneficial but not required.
Salary Range: $100,000 \- $160,000\. Individual pay will be based on several factors including experience, knowledge, skills, and abilities of the applicant.
Wight \& Company offers excellent benefits and a wonderful work environment. Our comprehensive benefits including medical, dental, vision, disability, life insurance, flexible spending, paid holidays, paid time off, and a 401(k). Our flexible work schedule allows for a healthy work\-life balance. For more information about our company, visit our website at www.wightco.com.
Wight \& Company is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetics, protected veteran status, disability status, or any other characteristic protected by federal, state, or local laws.
Wight \& Company values diversity in our workforce. We are committed to recruiting, hiring, and promoting people with disabilities and veterans. If you need an accommodation to assist with completing the electronic application, please call us at 630\.969\.7000 and ask to speak with a Human Resources representative.
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Salary Context
This $100K-$160K range is below the median for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Wight & Company, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($130K) sits 30% below the category median. Disclosed range: $100K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Wight & Company AI Hiring
Wight & Company has 1 open AI role right now. They're hiring across Data Engineer. Based in Darien, IL, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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