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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 looking for a skilled and innovative AI Data Engineer to join a team focused on modernizing how roadmap and portfolio data is managed, automated, and reported. In this role, you will leverage artificial intelligence and automation to streamline complex workflows across multiple platforms, eliminate manual data processes, and replace static slide- and spreadsheet-based reporting with dynamic, live dashboards. You will serve as a key contributor in building a governed single source of truth for hardware planning data, enabling smarter decision-making through AI-enabled solutions and real-time business intelligence.
Job Responsibilities:
- Assess current workflows, baseline manual effort, and prioritize automation opportunities by impact using AI-driven approaches
- Integrate data from multiple platforms and cross-organization databases into a governed single source of truth with automated refresh and data-quality checks
- Build, maintain, and troubleshoot Power BI dashboards to replace manual reporting processes
- Develop and deploy AI use cases including change summaries, data reconciliation, natural-language Q&A, and report automation
- Aggregate and manage data across SharePoint, Azure DevOps, Excel, and cross-organizational systems
- Document solutions, train end users, and report measurable impact including hours saved, cycle-time reduction, and data quality improvements
- Translate business needs into technical solutions and executive-ready visualizations
Requirements:
- 4+ years of experience in data engineering, business intelligence, or analytics automation
- 2+ years of experience building and deploying production generative AI solutions in an enterprise environment
- Demonstrated track record of implementing AI use cases to optimize workflows and improve operational efficiency
- Previous experience in a large enterprise or technology environment is strongly preferred
- Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field, or equivalent practical experience
- Microsoft certifications in Power BI, Fabric, Power Platform, or Azure AI are preferred
- Expert-level Power BI including data modeling, DAX, Power Query (M), deployment, refresh management, and row-level security
- Proficiency with Azure DevOps queries, Analytics/OData, and REST APIs
- Hands-on experience with Power Automate, SharePoint Online, and the broader Microsoft Power Platform including Power Apps
- Production-level experience building generative AI solutions using tools such as Copilot Studio, Azure OpenAI, AI Builder, or Microsoft 365 Copilot
- Strong proficiency in SQL, Python, and advanced Excel
- Experience aggregating and reconciling data across multiple enterprise platforms and systems
- Clear and effective communication skills with the ability to present complex technical concepts to non-technical stakeholders
- Familiarity with Microsoft Fabric, Dataverse, KQL, Power Platform ALM, and Git is a plus
- Experience with hardware, supply-chain, or product roadmap and portfolio data is a plus
Additional Details:
- Location: Remote
- Duration: 7 months
- Pay Range*: $46.50 - $52.50 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 [email protected]*. 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 $95K-$108K range is in the lower quartile for Data Engineer roles in our dataset (median: $145K across 47 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 11,520 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 $177,000 based on 195 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($101K) sits 42% below the category median. Disclosed range: $95K to $108K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Microsoft AI Hiring
Microsoft has 84 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Research Scientist, AI Product Manager. Positions span Mountain View, CA, Singapore, Singapore, Remote, US. Compensation range: $108K - $331K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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