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About This Role
At T. Rowe Price, we identify and actively invest in opportunities to help people thrive in an evolving world. As a premier global asset management organization with more than 85 years of experience, we provide investment solutions and a broad range of equity, fixed income, and multi\-asset capabilities to individuals, advisors, institutions, and retirement plan sponsors. We take an active, independent approach to investing, offering our dynamic perspective and meaningful partnership so our clients can feel more confident.
We believe doing the right thing for our clients and our associates is good business. With a career at the firm, you can expect opportunities to create real impact at work and in your community. You’ll enjoy resources to support your career path, as well as compensation, benefits, and flexibility to enrich your life. Here, you’ll find a collaborative culture that respects and values differences and colleagues who share a spirit of generosity.
Join us for the opportunity to grow and make a difference in ways that matter to you.
Role Summary
T. Rowe Price is seeking an innovative Data AI Engineer to guide the development and deployment of scalable AI and data solutions that enable next generation data foundations and capabilities leveraging AI for data and data for AI. The successful candidate will be a hands\-on contributor with deep technical skills, who can drive best practices and innovation in a collaborative, purpose\-driven environment.
Responsibilities
- Technical design, development, and maintenance of advanced “AI for data” and “Data for AI” capabilities driving advanced data pipelines, analytics platforms, and AI/ML solutions.
- Collaborate with diverse set of team members varying from business stakeholders, data scientists, product owners, and other stakeholders to understand business requirements and translate them into robust technical solutions.
- Delivery of AI data foundations in the data supply chain process and data storage systems to ensure high\-quality, reliable, and compliant data across the enterprise.
- Build and operationalize machine learning models, facilitating their integration into business workflows and production environments.
- Champion data governance, security, and regulatory compliance, ensuring alignment with T. Rowe Price’s standards and industry best practices.
- Evaluate and implement emerging technologies, frameworks, and tools to advance T. Rowe Price’s data and AI capabilities.
- Troubleshoot and optimize data solutions and platform performance, ensuring scalability and resilience.
- Document system architectures, processes, and best practices for technical and non\-technical stakeholders.
Qualifications
Required:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)
- 2\+ years of professional experience in data engineering, AI, or machine learning roles.
- Strong proficiency in Python (preferred), Java, or Scala.
- Experience with cloud data platforms (AWS, Azure, or GCP), preferably within a regulated industry.
- Hands\-on expertise with big data technologies (Spark, Kafka) and modern data platforms (Snowflake)
- Proven track record deploying and maintaining machine learning models in production environments.
- Solid understanding of Agile methodologies and DevOps practices.
- Excellent communication, collaboration, and leadership skills.
Preferred:
- Experience in the asset management or financial services industry.
- Familiarity with MLOps, CI/CD pipelines, and model lifecycle management.
- Industry certifications in cloud or data engineering technologies.
- Knowledge of data privacy, compliance (e.g., GDPR), and industry regulations relevant to financial services.
FINRA Requirements
FINRA licenses are not required and will not be supported for this role.
Work Flexibility
This role is eligible for hybrid work, with up to three days per week from home.
WHAT TO EXPECT AFTER APPLYING
1\. You will receive an email and text message to answer a few questions to verify your eligibility. If you apply for multiple jobs, you will receive separate invitations for each role and will need to respond to each. Estimated Time Commitment: 3\-5 minutes
2\. If you are eligible, you will be asked to record video responses to introductory questions with our vendor partner, HireVue. Take time to read all instructions carefully before responding. Some questions may require you to respond within a set time limit, or with limited retakes. You can use this opportunity to tell us more about your background and interest than we can learn from a resume alone. Estimated Time Commitment: 20\-30 minutes
Base Salary Ranges
Please review the job posting for the location of this specific opportunity.
$97,000\.00 \- $165,000\.00 for the location of: Maryland, Colorado, Washington and remote workers
$106,000\.00 \- $182,000\.00 for the location of: Washington, D.C.
$121,000\.00 \- $206,000\.00 for the location of: New York, California
Placement within the range provided above is based on the individual’s relevant experience and skills for the role. Base salary is only one component of our total compensation package. Employees may be eligible for a discretionary bonus, which is determined upon company and individual performance.
Commitment to Diversity, Equity, and Inclusion
At T. Rowe Price, our associates are our greatest asset. We thrive because our company culture is built on inclusion and because we sustain a work environment where associates can bring their best selves to work every day. The backgrounds, talents, and experiences of our global associates allow us to embrace new ideas and perspectives that move our business priorities forward and enable us to deliver strong client outcomes. Here, you can expect equal opportunity and fair and consistent treatment for all.
Benefits
We value your goals and needs, at work and in life. As an associate, you’ll be supported with resources, benefits, and work\-life balance so you can thrive in ways that matter to you.
Featured employee benefits to enrich your life:
- Competitive compensation
- Annual bonus eligibility
- A generous retirement plan
- Hybrid work schedule
- Health and wellness benefits, including online therapy
- Paid time off for vacation, illness, medical appointments, and volunteering days
- Family care resources, including fertility and adoption benefits
Learn more about our benefits.
T. Rowe Price is an equal opportunity employer and values diversity of thought, gender, and race. We believe our continued success depends upon the equal treatment of all associates and applicants for employment without discrimination on the basis of race, religion, creed, color, national origin, sex, gender, age, mental or physical disability, marital status, sexual orientation, gender identity or expression, citizenship status, military or veteran status, pregnancy, or any other classification protected by country, federal, state, or local law.
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Salary Context
This $97K-$206K range is below the median for Data Engineer roles in our dataset (median: $160K across 37 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 3,823 AI roles we're tracking, Data Engineer positions make up 1% of the market. At T. Rowe Price, 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 $208,300 based on 266 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($151K) sits 27% below the category median. Disclosed range: $97K to $206K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
T. Rowe Price AI Hiring
T. Rowe Price has 1 open AI role right now. They're hiring across Data Engineer. Based in Baltimore, MD, US. Compensation range: $206K - $206K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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