Practice Architect - Life Sciences

Location

USA

Job Description/Requirements:

As a Practice Architect – Life Sciences, you will be responsible for designing and implementing data engineering solutions for life science organizations. They collaborate with clients to understand their business needs and create custom solutions that meet their requirements. They also provide guidance and expertise to internal teams to ensure that data engineering projects are completed on time and within budget.

• Lead the design and implementation of data engineering solutions for life science organizations.
• Collaborate with clients to understand their business needs and create custom solutions that meet their requirements.
• Provide guidance and expertise to internal teams to ensure that data engineering projects are completed on time and within budget.
• Develop and maintain data engineering standards and best practices.
• Identify and recommend new technologies and tools to improve the efficiency and effectiveness of data engineering projects.
• Develop and maintain relationships with key stakeholders in the life science industry.
• Provide thought leadership and subject matter expertise in data engineering for life science organizations.

Education:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Technical certification in multiple technologies is desirable.

Mandatory Skills:

• Strong knowledge of data engineering tools and technologies, including ETL, data warehousing, data integration, data processing pipelines.
• Experience working with life science data, at least one of clinical trial data, genomics data, or electronic health records.
• Experience with cloud-based data engineering solutions, such as AWS, Azure, or Google Cloud.
• Minimum of 10 years of experience in data engineering, with a focus on life science organizations.
• Knowledge of life science industry regulations and compliance requirements, such as HIPAA and GDPR.
• Strong communication and collaboration skills, with the ability to work effectively with clients and internal teams.
• Ability to lead and manage data engineering projects from start to finish.
• Strong analytical and problem-solving skills, with a focus on data quality and accuracy.