Sr. Data Engineer

Design, build, and maintain robust ETL/ELT pipelines and scalable batch and streaming data pipelines using cloud platforms like Snowflake or Databricks.

Support embedding generation and vector-based data pipelines for AI and GenAI use cases and develop semantic data models for trustworthy analytics-ready datasets.

Provide technical leadership and mentorship to other data engineers and collaborate with cross-functional teams across manufacturing, quality, commercial, and clinical domains.

Bachelor’s Degree or higher in Computer Science, Mathematics, Engineering, or related technical field with 6-8 years of related experience OR High School Diploma/GED with 10 years of related experience.

Strong experience with SQL, relational and NoSQL databases, and proficiency in Python including data-frame libraries like Pandas or Polars.

Hands-on experience building and maintaining large-scale data pipelines in cloud environments (Azure or AWS) and working with Databricks or Snowflake and distributed data processing frameworks such as Spark.

Experience managing data quality, validation, lineage, and working with ETL/workflow orchestration tools like Databricks Workflows, Azure Data Factory, or AWS Glue.

Experienced data engineer with strong skills in cloud-based data platforms, ETL pipeline design, and data quality management, comfortable working in regulated healthcare or life sciences contexts.

Candidate who can lead technical efforts and mentor others while effectively collaborating with diverse global stakeholders across AI/ML, product, analytics, and business teams.

Familiarity with AI/ML data use cases, vector databases, feature stores, and an understanding of data privacy, security, and compliance in enterprise healthcare environments.