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About This Role
Employment Type:
ALLETE \- Non\-Union
FLSA Status:
Exempt
AI Data Engineer II
$82,000 \- $102,000 \| Hybrid with in\-person reporting to Duluth, MN
Shape the future of AI through data. Build the data foundation that powers AI solutions across the organization.
We’re seeking a skilled professional to design and deliver the data pipelines, integrations, and scalable datasets that enable AI solutions to thrive. In this role, you’ll help power AI applications, agents, and intelligent automations by ensuring data is reliable, secure, accessible, and ready for real\-world use. Working closely with AI Engineers, Data Analytics, and business teams, you’ll transform complex data into trusted assets that drive innovation, insights, and operational efficiency across the organization.
What You’ll Do
- Design, build, and support scalable data pipelines, integrations, datasets, and data flows that enable AI solutions and analytics.
- Prepare, transform, validate, and integrate data from enterprise systems, APIs, databases, Microsoft 365, Azure, and Power platform technologies.
- Collaborate with AI Engineers, Data Analytics, and business teams to ensure data readiness, quality, accessibility, and alignment with AI use cases and production solutions.
- Develop and maintain data models, transformation logic, reporting, documentation, and AI capabilities such as document ingestion, retrieval, embeddings, and analytics workflows.
- Apply testing, monitoring, security, privacy, governance, and responsible AI practices to deliver reliable, compliant, and measurable data solutions.
Required Education and Experience
- Bachelor’s degree in computer science, information technology, data science, analytics, software engineering, computer engineering, or information systems required.
- Four or more years of experience in data engineering, analytics engineering, business intelligence, systems integration, data analysis, cloud technology, or related IT delivery required.
- This position may be subject to assessment of skills, job match and/or aptitude.
Preferred Experience
- Experience working with SQL and one or more programming or scripting languages such as Python, PySpark, JavaScript/TypeScript, C\#, or similar.
- Experience working with databases, APIs, data pipelines, data models, and enterprise integrations.
- Familiarity with cloud data technologies such as Azure data services, Microsoft Fabric, Power Platform, Dataverse, or similar platforms.
- Understanding of AI and data management concepts, including retrieval\-augmented generation (RAG), embeddings, vector search, data quality, governance, and secure data handling.
- Strong analytical, troubleshooting, communication, testing, documentation, and operational support skills.
Ready to help power the future of AI at ALLETE? Apply today —we’re hiring now to support immediate AI solution delivery across the business.
ALLETE is a values‑driven energy company advancing a sustainable future through its nationwide family of clean‑energy businesses and regulated utilities, all grounded in a culture of integrity, safety, people, and the planet. Across the organization, teams contribute to meaningful work that supports reliable energy, innovative infrastructure, and a long‑term commitment to sustainability.
We’re headquartered in Duluth, Minnesota, which is located on the scenic shores of Lake Superior. Duluth offers a unique combination of professional opportunity and outdoor adventure and is a welcoming place to live and build your career.
The expected annual compensation range for this position is $82,000 \- $102,000\. Compensation offered will vary based on knowledge, skills, experience, and market conditions.
This position qualifies for a comprehensive benefits package including:
- Incentive Programs
- Retirement Benefits
- Medical, Dental \& Vision Plans
- Health Savings Account
- Flexible Spending Accounts
- Life Insurance
- Disability
- Tuition Reimbursement
- Voluntary Benefits
- Paid Absences, and more.
This position is posted externally as an AI Data Engineer II and is internally classified as a Programmer Analyst II.
Employer will not sponsor Visas for position.
*External applicants must apply online via www.allete.com/careers.*
*This job posting will be available for application until the position has been filled OR the posting close date noted herein, whichever date is earlier.*
*ALLETE is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, gender identity, sexual orientation, age, status as a protected veteran, among other things, or status as a qualified individual with disability.*
*If you are an individual with disabilities who needs accommodation or you are having difficulty using our website to apply for employment, please contact our Human Resources department at 218\-723\-3921\.*
*EEO/AA/F/M/Vet/Disabled*
Salary Context
This $82K-$102K range is in the lower quartile for Data Engineer roles in our dataset (median: $150K across 15 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 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At ALLETE, 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 $178,800 based on 40 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($92K) sits 49% below the category median. Disclosed range: $82K to $102K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
ALLETE AI Hiring
ALLETE has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Based in Duluth, MN, US. Compensation range: $102K - $102K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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