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About This Role
About Blueprint
Blueprint is a technology solutions firm headquartered in Bellevue, Washington, with teams across the United States. We help organizations turn complex challenges into meaningful outcomes by connecting strategy and execution across AI, cloud, data, product development, and emerging technology.
Our culture is built by people who care deeply about doing exceptional work. We set high standards, take ownership, and continually challenge ourselves and one another to be better. We work hard, support each other, and take genuine pride in what we deliver for our clients, partners, and teams.
At Blueprint, you'll work alongside talented people with different experiences, expertise, and perspectives. You'll have opportunities to take on meaningful challenges, expand your skills, and see the impact of what you build.
Bring your perspective. Raise the standard. Build what matters.
In This Role
You will help modernize data management, workflow automation, and reporting for complex cloud infrastructure and hardware planning initiatives. By combining AI-powered solutions, data engineering, and business intelligence, you will transform manual, spreadsheet-based processes into efficient, automated, and accessible reporting systems. You will integrate data across multiple platforms, build and maintain Power BI dashboards, and identify opportunities to improve data quality, reduce manual effort, and accelerate decision-making. This role offers an opportunity to apply emerging AI technologies to real-world business challenges while building expertise in cloud infrastructure and enterprise data operations.
Responsibilities
- Assess existing business workflows, establish baselines for manual effort, and identify opportunities for AI-driven automation based on business impact.
- Integrate and consolidate data from SharePoint, Azure DevOps, Excel, and cross-functional databases into a centralized, governed source of truth.
- Develop, maintain, and troubleshoot Power BI dashboards that deliver timely, accurate, and actionable business insights.
- Build and implement AI-powered solutions for workflow automation, data reconciliation, change summaries, natural-language querying, and report generation.
- Automate data refresh processes and implement data validation, quality checks, and reporting controls to improve reliability and consistency.
- Use Power Platform tools and integrations to streamline data collection, processing, reporting, and operational workflows.
- Collaborate with stakeholders to translate business requirements into scalable technical solutions and clear, executive-ready visualizations.
- Document technical solutions, develop user guidance, and support adoption of automated workflows and reporting tools.
- Measure and communicate the impact of implemented solutions through metrics such as hours saved, cycle-time reduction, and improvements in data quality.
- Continuously evaluate existing dashboards and processes to identify opportunities for optimization, efficiency, and improved user experience.
Qualifications
- Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field, or equivalent practical experience.
- Experience in data engineering, business intelligence, analytics automation, or a related technical discipline.
- Demonstrated experience implementing AI solutions to improve workflows, automate repetitive tasks, or increase operational efficiency.
- Hands-on experience with Power BI, including data modeling, DAX, Power Query, dashboard development, data refresh, deployment, and row-level security.
- Experience with Microsoft Power Platform tools, including Power Automate and Power Apps, to support business process automation and data-driven solutions.
- Familiarity with Azure DevOps, including queries, Analytics/OData, and REST API integrations.
- Experience working with SharePoint Online, advanced Excel, SQL, and Python for data integration, analysis, transformation, or automation.
- Ability to aggregate and reconcile data from multiple platforms and systems while maintaining data accuracy and consistency.
- Experience developing or implementing generative AI solutions using tools such as Copilot Studio, Azure OpenAI, AI Builder, or Microsoft 365 Copilot.
- Strong analytical, troubleshooting, and problem-solving skills, with the ability to identify process inefficiencies and recommend practical improvements.
- Clear communication skills, including the ability to explain technical solutions, document workflows, and present data insights to business stakeholders.
- Ability to demonstrate a previous AI implementation through a live project walkthrough, without disclosing confidential employer, customer, or proprietary information.
Preferred Qualifications
- Experience working with hardware planning, cloud infrastructure, supply-chain data, product roadmaps, or portfolio reporting.
- Experience optimizing AI workloads or implementing AI-enabled workflow improvements in an enterprise environment.
- Experience consolidating data across organizational systems into a centralized reporting or analytics solution.
- Familiarity with Microsoft Fabric, Dataverse, or Kusto Query Language (KQL).
- Experience with Power Platform application lifecycle management (ALM), Git, or related version control practices.
- Experience developing governed, scalable data solutions with automated refresh processes and data-quality monitoring.
- Relevant certifications in Power BI, Microsoft Fabric, Power Platform, or Azure AI.
Salary Range
At Blueprint, we strive to offer competitive pay that reflects the value of our team members. Compensation for this role is influenced by a variety of factors, including skills, education, responsibilities, experience, and geographic market. For candidates based in Washington State, the anticipated salary range is $90000– $95,000 USD/Annually. Please note that we typically do not hire new employees at the top of the posted range. Actual starting pay will be determined based on experience, skills, and internal equity. The final salary and job title may vary depending on the selected candidate's qualifications and could fall outside the stated range.
Location:Remotw
Benefits
Blueprint believes that healthy, supported employees do their best work. Eligible employees have access to a comprehensive benefits package that may include:
- Medical, dental, and vision coverage
- Flexible Spending Account (FSA)
- 401(k) retirement plan
- Competitive paid time off
- Parental leave
- Professional growth and development opportunities
Benefits and eligibility may vary based on role, employment status, and location.
Equal Employment Opportunity
Blueprint Technologies, LLC is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, sex, pregnancy, childbirth or related medical conditions, sexual orientation, gender identity or expression, national origin, ancestry, age, disability, genetic information, marital or familial status, military or veteran status, citizenship status, or any other characteristic protected by applicable law.
Applicant Accommodations
If you need a reasonable accommodation to participate in any part of the application or interview process, please contact [email protected].
Salary Context
This $90K-$95K 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 Blueprint Technologies, 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 ($92K) sits 48% below the category median. Disclosed range: $90K to $95K.
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.
Blueprint Technologies AI Hiring
Blueprint Technologies has 3 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, AI Product Manager. Positions span Redmond, WA, US, Charlotte, NC, US. Compensation range: $95K - $145K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 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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