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Data Engineer with responsibility to configure non-GMP computer systems used by Manufacturing Science & Technology (MSAT) department staff to organize manufacturing process information and to assist with bioprocess monitoring and analysis.

This employee should be passionate about the intersection of data and life sciences. They should actively apply both data engineering and software development skills to solve problems.

Job Responsibilities

Work closely with MSAT staff to understand workflows and questions that originate in manufacturing and laboratory environments.

Work as part of a larger data engineering team to develop analysis computer systems and tools with the overall goal to improve E2E operational performance (e.g., capacity to be freed up, quality, yield, productivity, lead times; exact KPIs) at various manufacturing sites across the globe.

Standard data engineering tasks: obtain data extracts and define secure data exchange approaches, perform data quality checks and contribe to the data pipeline.

Acquire, ingest, and process data from multiple sources and systems into Big Data platforms.

Collaborate with data scientists to map data fields to hypotheses and curate, wrangle, and prepare data for use in their advanced analytical models.

Skills Preferred:

1 year of work experience in current good manufacturing practice biopharmaceutical production setting (process development and/or manufacturing technical support).

Some knowledge of statistical process control and data analysis techniques

The ideal candidate will have a general knowledge of the underlying scientific principles applied to the development and manufacture of biopharmaceuticals. They will have a keen interest in learning bioprocess operations.

Proficiency in data analysis techniques and a willingness to learn new techniques is desired.

Experience with the following technologies: Distributed Processing (Spark, Hadoop, EMR), traditional RDBMS (MS SQL Server, Oracle, MySQL, PostgreSQL), MPP (AWS Redshift, Teradata), NoSQL (MongoDB, DynamoDB, Cassandra, Neo4J, Titan)

Ability to work across structured, semi-structured, and unstructured data, extracting information and identifying linkages across disparate data sets

Experience and interest in Cloud platforms

Experience in traditional data warehousing and deploying ETL processes with Python.

Familiarity with agile ways of working e.g. in SCRUM-teams.

Education

Minimum:

BS or higher degree in Chemical Engineering, Biochemical Engineering, Biochemistry, Computer Science, related field or equivalent work experience

Experience translating scientific or engineering workflows into automated systems.

4 years of work experience with Python, C#, Javascript, or other programming language.

2 years of work experience with SQL and data manipulation.