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About This Role
AI Data Engineer (Contract)
*Please note that this is a contract role providing services to Microsoft through external staffing partners of Allegis Global Solutions. If you are selected for this role, you will be employed by AGS and will not be an employee of Microsoft.*
Summary:
We are seeking a highly skilled Data Analyst and Business Intelligence professional to join a dynamic team focused on transforming complex operational and infrastructure data into trusted, actionable insights. In this fully remote role, you will serve as a critical bridge between data, operations, and business stakeholders — ensuring that data is reliable, meaningful, and accessible. You will analyze large datasets related to power, cooling, environmental systems, and critical infrastructure, while designing scalable reporting solutions and automating manual processes to drive operational excellence and strategic decision\-making.
Job Responsibilities:
- Partner with engineering, operations, program management, and leadership teams to define data requirements and develop analytics solutions that drive informed decision\-making
- Analyze large and complex datasets to identify trends, anomalies, gaps, risks, and opportunities for operational improvement
- Perform data quality assessments, validation, and verification to ensure accuracy, completeness, consistency, and integrity across multiple sources and systems
- Design, implement, automate, and maintain scalable data collection, transformation, and validation processes that reduce manual effort and improve reliability
- Develop and maintain dashboards, scorecards, KPI reporting, and self\-service analytics solutions that provide visibility into operational performance, telemetry health, and business outcomes
- Create data validation frameworks, monitoring processes, and quality controls to proactively detect and resolve data issues
- Collaborate with technical teams to improve data models, metadata management, reporting structures, and overall data governance practices
- Translate complex technical findings into clear, actionable insights and recommendations tailored for both technical and executive audiences
- Drive continuous improvement through automation, standardization, and implementation of scalable reporting and analytics solutions
- Support root cause analysis and investigative efforts by leveraging data to identify underlying drivers of performance issues and operational risks
- Document processes, methodologies, reporting requirements, and data quality standards to ensure consistency and scalability
Requirements:
- 4\+ years of experience conducting data quality verification to ensure accuracy, completeness, and consistency across critical infrastructure data sources
- 4\+ years of experience performing data analysis to identify trends, anomalies, gaps, and performance opportunities
- 3\+ years of experience developing intuitive dashboards and reports providing clear visibility into metrics, operational health, and program outcomes
- Experience in business intelligence, reporting, data quality management, or related analytical disciplines
- Background working with data center or equivalent critical infrastructure environments including power distribution and environmental systems strongly preferred
- Bachelor's degree in Computer Science, Computer Engineering, or a related technical field required
- 5–7 years of applicable professional experience required
- Strong data quality validation, verification, and monitoring expertise with the ability to ensure accuracy and integrity across multiple data sources
- Proficiency developing dashboards and visualizations using business intelligence tools such as Power BI, Tableau, or similar platforms
- Experience analyzing telemetry, operational, IoT, industrial controls, or critical infrastructure data including power and cooling systems
- Ability to automate manual processes using scripting, workflow automation, or data integration technologies
- Strong troubleshooting, problem\-solving, analytical thinking, and investigative skills with a focus on identifying root causes
- Experience developing executive\-level dashboards and KPI scorecards that communicate complex findings to diverse audiences
- Solid written and verbal communication skills with the ability to present insights clearly to both technical and non\-technical stakeholders
- Demonstrated success delivering scalable solutions that improve efficiency, data accuracy, and operational visibility
- Experience with data center or critical infrastructure health and performance data is highly desirable
- Familiarity with automated data validation and exception management processes
Additional Details:
- Location: Remote
- Duration: 12 months
- Pay Range\*: $68\.50 \- $77\.50 per hour
- Weekly Schedule: 40 hours
- Job Status: Non\-Exempt
- Application Deadline: Apply within 72 hours of the posting date to ensure consideration.
This role is eligible for the following benefits:
- Medical, dental \& vision
- Hospital plans
- 401(k) Retirement Plan – Pre\-tax and Roth post\-tax contributions available
- Life Insurance (Company paid Basic Life and AD\&D as well as voluntary Life \& AD\&D for the employee and dependents)
- Company paid short and long\-term disability
- Health \& Dependent Care Spending Accounts (HSA \& DCFSA)
- Employee Assistance Program
- Time Off/Leave(PTO, Allegis Group Paid Family Leave, Parental Leave
*Benefits are subject to change and may be subject to specific elections, plan, or program terms.*
*AGS is an Equal Opportunity Employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.*
*If you require a reasonable accommodation related to the application or interview process due to a disability, please email* *accommodation@allegisglobalsolutions.com**. This inbox is monitored solely for accommodation requests. For questions about open roles or to apply, please submit your application through the job posting, as this inbox is not monitored by recruiters and applications sent here will not be reviewed.*
*In accordance with the Immigration Reform and Control Act of 1986, employment is contingent upon verification of identity and authorization to work in the United States. All persons hired will be required to complete Form I\-9 and provide acceptable documentation as required by law.*
*Please note that we may use artificial intelligence (AI) tools to screen, assess, or select applicants for this position. These tools may analyze application materials and assist our team in identifying candidates whose qualifications best match the requirements of the role. If you have questions about our use of AI in the hiring process, or would like more information, please contact us.*
- *We reserve the right to pay above or below the posted wage based on factors unrelated to protected classifications.*
*Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job\-related factors.*
Salary Context
This $141K-$160K range is above the median 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 Microsoft, 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 ($150K) sits 16% below the category median. Disclosed range: $141K to $160K.
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.
Microsoft AI Hiring
Microsoft has 29 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span US, Redmond, WA, US, Dallas, TX, US. Compensation range: $143K - $304K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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