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Job Description
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Owns the Fabric data plane: the certified data and semantics every agent and BI use case depends on. The Agilent Intelligence Fabric is a single governed substrate serving both BI and agentic AI; one substrate, two consumption modes. This role makes the data side of that promise real, meaning every certified data product carries a semantic definition, a data contract, policy and entitlement metadata including agent identity, lineage and observability, and a certification tier.
The role operates on a core conviction of the program: AI is the primary builder of the Fabric, not merely its consumer. This leader deploys agents that generate metadata, resolve entities across domains, score quality, and classify unstructured content, so the data plane compounds in richness with every interaction rather than depending on manual annotation at enterprise scale.
Responsible for
- Certified, versioned data products and semantic models, built in partnership with domain owners and stewards, with certification tiers that agents and BI consumers can both trust.
- The Asset Registry, lineage, and data\-quality signals; the registry is the discoverable, versioned home for data products and semantic definitions.
- Lakehouse, vector, and graph retrieval foundations underpinning grounded agent behavior.
- Agentic workloads that build the Fabric itself: metadata generation, entity resolution, quality scoring, and unstructured content classification.
- Solid\-line management of AI Data Engineers deployed into pods.
- Leads a team responsible for designing, developing, and implementing modular data models, data pipelines, and data management frameworks that enable the capture, integration, storage, and utilization of structured and unstructured data from multiple sources.
- Applies in\-depth understanding of business and technical requirements to define data engineering priorities, direct the development of scalable data solutions, and establish standards and processes that ensure data reliability, efficiency, quality, compatibility, and accessibility.
- Provides technical and organizational leadership in the development of data platforms and tools that support analytics, data science, predictive and prescriptive modeling, and automation initiatives, while overseeing project execution, cross\-functional collaboration, talent development, and continuous improvement of data engineering capabilities to meet evolving business and product requirements.
Qualifications
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- Bachelor’s or Master’s Degree or equivalent. Plus, broad knowledge of functional area(s) of responsibility.
- Minimum of 10 years' experience formally or informally leading people, projects and/or programs.
- A track record building enterprise data platforms that serve production AI systems, not only analytics; experience with semantic layers, ontologies, or knowledge representation at scale.
- Deep familiarity with the modern lakehouse, vector, and graph landscape; experience with Microsoft Fabric, Snowflake, or equivalent platforms in a multi\-cloud estate.
- Experience operating data contracts, lineage, and certification models in a regulated or quality\-driven industry; life sciences or GxP exposure is a strong plus.
- Curiosity about AI, its potential and its pitfalls. The field moves monthly, and the people who thrive here are genuinely curious about both sides of it: what these systems can newly do, and where they fail, mislead, or quietly degrade. We want people who read the failure analyses as eagerly as the launch posts, who experiment on their own initiative, and who hold excitement and skepticism at the same time without letting either one win permanently.
- Lifelong learners. Whatever expertise a candidate arrives with will be partially obsolete within a year, and that is not a defect of the candidate; it is the condition of the field. We hire people who have reinvented their toolkit before and expect to do it again, who learn in public, and who treat being wrong as information rather than injury. A history of deliberate self\-reinvention counts for more than any single credential.
- Excellent communication and the ability to influence. Nothing in this organization ships by authority alone. Every role here persuades domain experts to engage, stewards to share what they know, sponsors to stay honest about value, and functions like Legal, Quality, and Security to move from gatekeeping to partnership. We look for people who write and speak clearly, who adapt their register from bench scientist to Board, and who change minds through credibility and clarity rather than escalation.
- The instinct to automate curation with AI rather than scale it with headcount.
Additional Details
This job has a full time weekly schedule. Applications for this job will be accepted until at least August 3, 2026 or until the job is no longer posted.
The full\-time equivalent pay range for this position is $171,600\.00 \- $321,750\.00/yr plus eligibility for bonus, stock and benefits. Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locations
Agilent Technologies, Inc. is an Equal Employment Opportunity and merit\-based employer that values individuals of all backgrounds at all levels. All individuals, regardless of personal characteristics, are encouraged to apply. All qualified applicants will receive consideration for employment without regard to sex, pregnancy, race, religion or religious creed, color, gender, gender identity, gender expression, national origin, ancestry, physical or mental disability, medical condition, genetic information, marital status, registered domestic partner status, age, sexual orientation, military or veteran status, protected veteran status, or any other basis protected by federal, state, local law, ordinance, or regulation and will not be discriminated against on these bases. Agilent Technologies, Inc., is committed to creating and maintaining an inclusive in the workplace where everyone is welcome, and strives to support candidates with disabilities. If you have a disability and need assistance with any part of the application or interview process or have questions about workplace accessibility, please email job\[email protected] or contact \+1\-262\-754\-5030\. For more information about equal employment opportunity protections, please visit www.agilent.com/en/accessibility.Travel Required:
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Administration
Salary Context
This $171K-$321K range is above the 75th percentile for Data Engineer roles in our dataset (median: $153K across 35 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 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Agilent 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 in Demand for This Role
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 $185,000 based on 83 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($246K) sits 33% above the category median. Disclosed range: $171K to $321K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Agilent Technologies AI Hiring
Agilent Technologies has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Santa Clara, CA, US, Wilmington, DE, US. Compensation range: $306K - $321K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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