Staff Data Engineer - Data Nexus
- Compensation
- Not disclosed
- Location
- New York, United States
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent practical experience.
- 8+ years of professional software or data engineering experience, including experience designing and operating large-scale production data systems.
- Strong understanding of modern data engineering patterns including ETL/ELT, Change Data Capture, streaming, data modeling, data migration, and distributed data processing.
- Strong software engineering fundamentals with proficiency in SQL and experience with languages such as Python, Java, Scala, or equivalent.
- Experience with modern cloud data platforms, transformation frameworks, and workflow orchestration technologies such as Snowflake, dbt, Airflow, or comparable platforms.
- Experience designing and operating cloud-native data solutions on AWS or another major cloud platform.
- Hands-on experience with streaming and lakehouse technologies such as Apache Flink, Kafka, Apache Iceberg, Amazon S3, and enterprise data catalog technologies such as AWS Glue or comparable platforms.
- Strong understanding of scalability, performance, security, data quality, observability, reliability, and production operations.
- Demonstrated ability to lead complex technical initiatives spanning multiple applications, systems, and engineering teams.
- Strong communication and technical leadership skills, including mentoring engineers and influencing architectural and engineering decisions.
- Practical experience incorporating AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, or equivalent tools into software engineering workflows.
Nice to have
- Experience building AI-enabled data applications and Generative AI use cases such as Retrieval-Augmented Generation (RAG), natural-language-to-SQL, conversational analytics, intelligent data discovery
- Experience with LLMs, embeddings, vector databases, metadata enrichment, knowledge retrieval, or similar AI/data architectures.
- Experience applying AI to data engineering use cases such as automated schema mapping, data transformation generation, data classification, anomaly detection, data-quality analysis, or intelligent pip
- Experience integrating AI/ML models or agentic workflows with enterprise data platforms, APIs, and production data pipelines.
- Familiarity with AI platform concepts such as model gateways, MCP, prompt and context management, evaluation, observability, security, and responsible AI practices.
- Experience with large-scale enterprise or clinical data migrations, analytics platforms, APIs, or reusable data products.
- Experience with infrastructure automation and modern engineering practices using technologies such as Kubernetes, Terraform, GitHub Actions, or equivalent.
- Experience in life sciences, healthcare, clinical trials, or another regulated environment.
Full description
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 . Listen to our latest podcast, from Dreamers to Disruptors, and follow us at @Medidata.
Our Team
The Data Nexus team within Medidata Study Experience builds the modern data foundation that connects clinical applications, customer data, analytics, and enterprise integrations.
Our mission is to make clinical operational data easier to ingest, migrate, transform, govern, and consume at scale. We build capabilities spanning large-scale customer and third-party study migrations, near-real-time CDC and streaming pipelines, reusable data products, analytics and reporting, and cross-product data integrations.
We are modernizing our platform around cloud-native and lakehouse architectures using technologies including AWS, Snowflake, dbt, Airflow, Apache Flink, Amazon S3, AWS Glue, and Apache Iceberg. We treat data as a product and build trusted, reusable capabilities that power Medidata applications, customer integrations, analytics, and emerging AI/ML experiences.
Who you are:
As a Staff Data Engineer, you will be a hands-on technical leader responsible for solving complex data engineering problems, influencing architecture and technical direction, driving strategic initiatives across teams, and raising engineering standards across Data Nexus.
What You'll Do
- Lead the architecture, design, and implementation of scalable data ingestion, migration, CDC, streaming, and ETL/ELT solutions.
- Provide technical leadership for large-scale customer and third-party clinical study data migrations.
- Design and build reusable data products and services supporting Medidata applications, analytics, reporting, AI/ML, and customer integrations.
- Drive modernization across cloud data warehousing, workflow orchestration, streaming, and lakehouse architectures.