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About This Role
Location: This is a hybrid remote/in-office role.
Medidata follows a hybrid office policy in which employees who are hired for an in-person position are expected to work on site a certain number of days per week following Company policy.
About our Company:
Medidata is powering smarter treatments and healthier people through digital solutions to support clinical trials. Celebrating over 25 years of ground-breaking technological innovation across more than 38,000 trials and 12 million patients, Medidata offers industry-leading expertise, analytics-powered insights, and one of the largest clinical trial data sets in the industry. More than 1 million registered users across approximately 2,300 customers trust Medidata's seamless, end-to-end platform to improve patient experiences, accelerate clinical breakthroughs, and bring therapies to market faster. A Dassault Systèmes brand (Euronext Paris: FR0014003TT8, DSY.PA), Medidata is headquartered in New York City and has been recognized as a Leader by Everest Group and IDC. Discover more at www.medidata.com. Listen to our latest podcast, from Dreamers to Disruptors, and follow us at @Medidata.
About the Team:
We are seeking a Lead AI & Data Engineer to design, build, and deploy production-grade AI solutions and data products. In this role, you will lead the development of AI-powered solutions leveraging large language models (LLMs), retrieval-augmented generation (RAG), agent orchestration, and modern data architectures to enable new AI-driven capabilities across our Virtual Twin ecosystem.
The ideal candidate combines deep data engineering and AI-native development expertise to build scalable, secure, and reliable AI-enabled data solutions. You will drive technical architecture, data pipeline development, AI integration, and engineering best practices, translating complex business and clinical research requirements into production-ready solutions. You will work closely with product management, data science, platform engineering, and clinical experts, to deliver innovative capabilities that enable intelligent workflows and data-driven decision-making.
Responsibilities:
- Engineer scalable, automated data pipelines and AI-powered solutions using modern AI technologies, vector databases, and automation, applying CI/CD practices and tools such as Python, SQL, and Git.
- Design, develop, and deploy production-grade AI-powered solutions leveraging LLMs, RAG, and agentic workflows.
- Implement and maintain integrations via API and MCP that connect applications and agents to enterprise data, tools, and skills in line with governance, security and compliance controls.
- Implement agent orchestration and workflow patterns using frameworks such as LangGraph, LangChain, or equivalent technologies.
- Develop agent-driven metadata management solutions to maintain data catalogs, business glossaries, lineage documentation, and governance policies.
- Apply advanced skills in data architecture, data science engineering, data modeling, and data quality using modern cloud-native technologies.
- Document and communicate technical work clearly to stakeholders at all levels, both technical and non-technical.
- Collaborate effectively in Agile environments and cross-functional teams
Qualifications:
- 7+ years of experience in data engineering or data science, with a strong foundation in enterprise data architecture, including at least 2 years of hands-on experience in AI and data engineering.
- Bachelor's degree in a technical or scientific field, such as Data Engineering, Data Science, Computer Science, or similar.
- Hands-on experience developing AI-native solutions using LLMs, RAG, AI agents, and agent orchestration patterns.
- Skilled in integrating AI/ML pipelines into production-grade data infrastructure with experience in API and MCP standards and integration, including model access controls, context management, auditability, security, compliance monitoring, and lifecycle management
- Experience implementing AI governance and guardrails, including access controls, data privacy, auditability, and policy enforcement across AI services and agents
- Proficiency in Python, SQL, and modern data engineering practices, including CI/CD and Git-based development.
- Hands-on experience with tools and concepts such as Airflow, CDC, batch processing, and job scheduling.
- Strong problem-solving, communication, and collaboration skills, with the ability to troubleshoot complex issues and to translate business requirements into secure, scalable, production-ready solutions.
- Experience with healthcare or clinical trial data is not required, but interest to learn and understand how these data improves Life Sciences is paramount.
Nice to Have:
- Experience with knowledge graphs, advanced vector search, and retrieval optimization techniques.
- Familiarity with clinical research data standards and formats such as CDISC SDTM and ADaM.
- Experience with AI evaluation frameworks, benchmarking, and continuous monitoring of AI solution performance.
- Experience optimizing cloud infrastructure, AI workloads, and data processing costs.
- Familiarity with semantic layers, metadata-driven architectures, and automated data cataloging.
The salary range posted below refers only to positions that will be physically based in New York City. As with all roles, Medidata sets ranges based on a number of factors including function, level, candidate expertise and experience, and geographic location. Pay ranges for candidates in locations other than New York City, may differ based on the local market data in that region.
The salary range range for this position physically based in NYC/ NJ Metro Area is $135,000-$180,000.
Base pay is one part of the Total Rewards that Medidata provides to compensate and recognize employees for their work. Most sales positions are eligible for a commission on the terms of applicable plan documents, and many of Medidata's non-sales positions are eligible for annual bonuses. Medidata believes that benefits should connect you to the support you need when it matters most and provides benefits, including medical, dental, life and disability insurance, 401(k) matching, family leave, flexible paid time off; and 10 paid holidays per year.
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Equal Employment Opportunity:
In order to provide equal employment and advancement opportunities to all individuals, employment decisions at Medidata are based on merit, qualifications and abilities. Medidata is committed to a policy of non-discrimination and equal opportunity for all employees and qualified applicants without regard to race, color, religion, gender, sex (including pregnancy, childbirth or medical or common conditions related to pregnancy or childbirth), sexual orientation, gender identity, gender expression, marital status, familial status, national origin, ancestry, age, disability, veteran status, military service, application for military service, genetic information, receipt of free medical care, or any other characteristic protected under applicable law. Medidata will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law.
We will accept applications on an ongoing basis until we fill the position.
Inclusion statement
In order to provide equal employment and advancement opportunities to all individuals, employment decisions at 3DS are based on merit, qualifications and abilities. 3DS is committed to a policy of non-discrimination and equal opportunity for all employees and qualified applicants without regard to race, color, religion, gender, sex (including pregnancy, childbirth or medical or common conditions related to pregnancy or childbirth), sexual orientation, gender identity, gender expression, marital status, familial status, national origin, ancestry, age (40 and above), disability, veteran status, military service, application for military service, genetic information, receipt of free medical care, or any other characteristic protected under applicable law. 3DS will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable state laws and local ordinances. We are committed to fair employment practices and will evaluate all candidates based on their qualifications, regardless of past arrest or conviction history. Salary Pay Transparency Compensation for the role will be commensurate with experience. The total expected compensation range will be between $135000 and $180000, representing the base salary (or annualized salary based on estimated hourly compensation) and target bonus.
Salary Context
This $135K-$180K range is above the median 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 Medidata Solutions, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($157K) sits 11% below the category median. Disclosed range: $135K to $180K.
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.
Medidata Solutions AI Hiring
Medidata Solutions has 2 open AI roles right now. They're hiring across Data Engineer. Based in New York, NY, US. Compensation range: $153K - $180K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national 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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