Location: This is a hybrid remote/in-office role.
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.
- Design scalable data models, transformation frameworks, APIs, and integration patterns for complex operational and analytical workloads.
- Improve data quality, observability, performance, reliability, security, and cost efficiency across the platform.
- Lead technical design and architecture discussions and collaborate across Product, Engineering, QA, Architecture, and other teams to deliver complex initiatives.
- Mentor engineers, review designs and code, and establish engineering patterns and best practices across the team.
- Leverage AI-assisted engineering tools such as GitHub Copilot, Claude Code, Cursor, and similar technologies to accelerate development, refactoring, testing, troubleshooting, and documentation.
- Identify opportunities to apply Generative AI and intelligent data experiences to improve how users discover, understand, and interact with clinical data.
Qualifications
- 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.
Preferred Qualifications
- 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, or semantic search.
- 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 pipeline troubleshooting.
- 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.
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 $135,000-180,000
- The salary range for positions that will be physically based in all other locations within the United States is $120,750-161,000.
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.
#LI-MM1
#LI-Hybrid
Note: Please be on the lookout for job scams. Medidata recruiters will never ask applicants for monetary compensation, credit card, or banking details.
We will accept applications on an ongoing basis until we fill the position.
