Staff Data Engineer – Data Nexus (Hybrid, AI-Enabled)

Medidata Solutions, Inc.

New York (NY)

Hybrid

USD 135,000 - 180,000

Full time

32 hours ago

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Benefits offered by this job

Medical insurance

Dental insurance

Life insurance

Disability insurance

401(k) matching

Family leave

Flexible PTO

10 paid holidays

Job summary

Medidata Solutions, Inc. is seeking a Staff Data Engineer to lead data ingestion, migrations, and lakehouse modernization.

You will architect scalable ETL/ELT and CDC solutions, mentor engineers, and collaborate across product and engineering teams to deliver data products that power analytics, AI/ML, and integrations. You will work in a hybrid setup, with a focus on scalable cloud data platforms (Snowflake, AWS, S3) and modern orchestration (Airflow), ensuring reliability and performance for

Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience.
8+ years of professional software or data engineering experience, including large-scale data systems.
Strong understanding of ETL/ELT, CDC, streaming, data modeling, migration, and distributed processing.
Proficiency in SQL and languages such as Python, Java, Scala, or equivalents.
Experience with Snowflake, dbt, Airflow, and comparable platforms.
Cloud data solutions on AWS or other major platforms.
Hands-on with streaming and lakehouse tech like Flink, Kafka, Iceberg, S3, and data catalogs.
Ability to lead initiatives across teams and mentor engineers.
Experience incorporating AI-assisted development tools into workflows.

Responsibilities

Lead architecture and implementation of scalable data ingestion, migration, CDC, streaming, and ETL/ELT solutions.
Provide technical leadership for large-scale data migrations.
Design reusable data products supporting Medidata applications and analytics.
Drive modernization across cloud data warehousing and lakehouse architectures.
Design data models, APIs, and integration patterns for complex workloads.
Improve data quality, observability, security, and cost efficiency.
Lead design discussions and collaborate across Product, Engineering, QA, and Architecture.
Mentor engineers and establish engineering best practices.
Apply AI-assisted tools to accelerate development and troubleshooting.