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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.
Our team:
This position is for the Data Nexus team within Study Experience Engineering, focused on building Medidata's next generation of AI-enabled data platforms and intelligent applications. The team works across Generative AI, Agentic workflows, AWS, Snowflake, Apache Iceberg, Airflow, Python, and Java to power scalable data ingestion, transformation, APIs, analytics, and automation. You will work in a highly collaborative environment with engineering, product, QA, PMO, and platform teams to solve complex data and application challenges and bring AI-driven capabilities into Medidata's clinical technology ecosystem.
Who we are looking for:
Reporting to the Director, Engineering, the Senior AI & Data Engineer – Study Experience will design, build, and operate production-grade AI and data solutions that enable intelligent applications, automation, analytics, and operational workflows.
This is a hands-on engineering role with a primary focus on Generative AI and agentic applications, supported by strong data and application engineering capabilities. You will build AI-enabled services, connect enterprise data and applications to LLM-powered workflows, and develop scalable data pipelines using modern cloud technologies.
What You'll Do
- Design and build production Generative AI applications, AI agents, and LLM-powered workflows for enterprise use cases.
- Develop AI services and backend capabilities using Python, APIs, and cloud-native engineering patterns.
- Build RAG and retrieval workflows that securely connect enterprise data and applications to AI solutions.
- Implement agentic workflows involving tool calling, structured outputs, retrieval, workflow execution, and external system integrations.
- Work with AI orchestration frameworks and emerging standards such as LangGraph, LangChain, MCP, or similar technologies.
- Implement testing, evaluation, guardrails, monitoring, and observability to improve the quality and reliability of production AI solutions.
- Design and maintain scalable data pipelines and ETL/ELT workflows that support AI, analytics, and operational applications.
- Build data solutions using AWS, Snowflake, Python, SQL, and modern data engineering technologies.
- Develop and integrate APIs and backend services, including Java-based applications where needed.
- Apply strong software engineering practices including automated testing, CI/CD, Git, security, documentation, and production support.
- Use AI-assisted development tools and coding agents as part of day-to-day design, coding, testing, debugging, and code review.
- Collaborate with engineers, architects, product teams, and business stakeholders on technical designs and production solutions.
- Mentor engineers through technical guidance, code reviews, and knowledge sharing.
What We're Looking For
- 5+ years of professional experience in software engineering, AI engineering, data engineering, or a related technical discipline.
- Strong hands-on experience developing production applications and services using Python.
- Practical experience building or integrating Generative AI applications, LLM workflows, agentic systems, or AI-enabled services.
- Good understanding of RAG, embeddings, vector search, retrieval, grounding, and LLM orchestration concepts.
- Experience with at least one AI orchestration framework or emerging protocol such as LangGraph, LangChain, MCP, or equivalent technologies.
- Strong SQL and data engineering fundamentals, including experience building production data pipelines and integrations.
- Experience working with AWS and modern data platforms such as Snowflake.
- Experience developing or integrating REST APIs and backend services.
- Strong understanding of modern software engineering practices including automated testing, CI/CD, version control, observability, and production support.
- Ability to independently troubleshoot complex issues across AI services, APIs, data pipelines, and cloud environments.
- Strong communication and collaboration skills and the ability to contribute effectively to technical design discussions.
Preferred Skills
- Experience with managed AI platforms or models such as AWS Bedrock, Anthropic Claude, OpenAI APIs, Azure OpenAI, or similar technologies.
- Experience with vector and search technologies such as OpenSearch, pgVector, Pinecone, Weaviate, or similar platforms.
- Experience building MCP servers, tools, or enterprise AI integrations.
- Familiarity with data technologies such as Airflow, Kafka, Apache Iceberg, or dbt.
- Experience with Docker, Kubernetes, Terraform, or similar cloud-native technologies.
- Familiarity with AI evaluation, prompt/version management, structured logging, and AI observability.
- Understanding of AI governance, data privacy, security, responsible AI, and human-in-the-loop patterns.
- Experience working in regulated industries such as Life Sciences or Healthcare.
As with all roles, Medidata sets ranges based on a number of factors including function, level, candidate expertise and experience, and geographic location.
- The salary range for positions that will be physically based in the NYC Metro Area is $114,750-153,000
- The salary range for positions that will be physically based in all other locations within the United States is $102,750-137,000.
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 $114750 and $153000, representing the base salary (or annualized salary based on estimated hourly compensation) and target bonus.
Salary Context
This $102K-$153K range is below the median for Data Engineer roles in our dataset (median: $140K across 33 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 9,079 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 180 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($127K) sits 28% below the category median. Disclosed range: $102K to $153K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Medidata Solutions AI Hiring
Medidata Solutions has 1 open AI role right now. They're hiring across Data Engineer. Based in New York, NY, US. Compensation range: $153K - $153K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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