Senior Data Engineer

Quantori; Madison (WI); On-site; USD 130,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Professional development

Job summary

Quantori seeks a Senior Data Engineer to design, build, and optimize data capture, processing, and storage solutions for advanced analytics. You will create robust, future-proof data systems and pipelines supporting scientific, technical, and operational decision-making.

The role focuses on scalable ETL, enterprise data models, and ensuring data readiness for AI/ML initiatives within a modern cloud ecosystem.

Qualifications

• Bachelor's degree in Engineering, Data Science, Life Sciences, Computer Science, or related field.
• 6+ years of experience in data engineering, including data modeling and database design, preferably in scientific/manufacturing/healthcare contexts.
• Proficiency with Python, R, SQL and cloud architectures (AWS, Snowflake, Databricks, Redshift) with Spark and dbt.
• Familiarity with Databricks AI/BI or Tableau or other BI tools.
• Expertise in ETL and data warehouse design.

Responsibilities

• Design, build, and maintain scalable data pipelines from diverse sources (lab systems, MES, clinical supply, quality systems, partners).
• Create and optimize data flows for structured/unstructured data using Python (PySpark), R, SQL, Databricks, Snowflake and related tools.
• Develop and maintain enterprise data repositories with robust data models and new models as needed.
• Enable AI/ML readiness by ensuring data is well-structured, versioned, traceable, and semantically aligned with data standards.

Skills

Python

R

SQL

AWS

Snowflake

Databricks

Redshift

Spark

dbt

Tableau

NoSQL

Graph DBs

English B2+

Education

Bachelor's degree in Engineering / Data Science / Life Sciences / CS

Tools

Databricks

Snowflake

Redshift

dbt

Tableau

AWS

Job description

We are seeking a Senior Data Engineer to design, build, and optimize data capture, processing, and storage solutions that enable advanced analytics and digital process transformation. This role involves creating robust, future‑proof data systems, engineering workflows, and high‑value data repositories that support scientific, technical, and operational decision-making.

Location: Madison, Wisconsin

Responsibilities:

• Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing data from diverse data generation sources and systems (e.g., lab systems, MES, clinical supply, quality systems, external partners).
• Create and optimize data flows for structured and unstructured data using Python (PySpark), R, SQL, Databricks, Snowflake, and other modern engineering tools.
• Develop and maintain specific data repositories, implementing enterprise‑level data models, and creating new models as needed.
• Enable AI/ML readiness by ensuring data is well‑structured, versioned, traceable, and semantically aligned with enterprise data standards.

What we expect:

• Bachelor’s degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred.
• 6+ years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment.
• Proficiency with Python, R, SQL, and cloud-based architectures (AWS services, Snowflake, Databricks, Redshift) along with Spark and dbt.
• Familiarity with Databricks AI/BI or Tableau or other BI tools.
• Expertise in ETL and DWH.
• Experience with NoSQL and graph databases.
• English language proficiency of B2+
• Strong analytical, problem‑solving, and stakeholder‑management skills, with the ability to translate discussions into actionable requirements.
• Ability to drive multiple exciting projects simultaneously with strong organizational skills and adaptability.

Nice to have:

• Experience with regulated or standards‑driven data environments, such as CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards.
• Familiarity with high‑dimensional data (e.g., imaging, sensor data, etc).
• Experience with principles connecting to or feeding MLOps and model deployment workflows.
• Knowledge of manufacturing systems (MES), laboratory information systems, or industrial data systems.
• Exposure to knowledge graph or ontology‑driven architectures.

We offer:

• Competitive compensation
• Continuous education, mentoring, and professional development programs