Senior Manager - Data Architect - Research & Development - Life Sciences/Pharma

Summery:

As a member of the Engineering team, your objective is to design and deliver scalable, robust, and innovative data & data product discovery solutions that meet the needs of scientists, researchers and executives. You will collaborate closely with cross-functional teams, including scientists, data engineers, software engineers and business solution partners, to understand requirements, translate them into technical designs, and guide the implementation of these solutions on cloud infrastructure. By leveraging your expertise in data & analytics engineering, you will contribute to the acceleration of scientific discovery and play a vital role in shaping the future of data & analytics.

What You Will Be Doing:

- Partnering across Pharma Research & Development stakeholders to develop an integrated data strategy for maximizing value derived from internal data assets and the external data ecosystem

- Participating as a data lead on multi-functional research and IT teams, prototyping, testing, and planning the design and integration of workflows applied to R&D data

Job Duties:

- Designing and developing data architecture frameworks and strategies to support life science instrument data collection, storage, and analysis.

- Collaborating with cross-functional teams, including researchers, scientists, and IT professionals, to understand data requirements and define data architecture solutions.

- Establishing data standards, guidelines, and best practices for data collection, integration, and validation to ensure data quality and integrity.

- Assessing and selecting appropriate data management tools, technologies, and platforms to support pharmacutiecal doamin data needs.

- Creating and maintaining data models, data dictionaries, and metadata repositories to facilitate data understanding and accessibility.

- Implementing data governance processes to ensure compliance with regulatory requirements and privacy policies.

- Collaborating with data scientists and statisticians to design and optimize data processing & analytical systems .

- Hands-on involvement in developing data models and architecture for critical engagements.

- Collaborate with DataOps enterprise architects to contribute to the enterprise design and delivery of efficient data solution architectures, automation and technology choices starting from experimentation through proof of concept and delivery.

- Proactively engage with the IT community (internally and externally) across multiple channels, looking to share, represent, educate, and inspire.

- Serve as a coach for IT Data Analysts/Engineers to advance individuals' technical acumen.

- Identifying opportunities for data integration and harmonization across different research projects and data sources.

- Staying updated with industry trends and advancements in data architecture, data management, and data science to drive innovation and enhance pharma and life-science data domain capabilities.

- Identify, design, and implement process improvements opportunity by automating manual processes and optimizing data delivery.

JOB REQUIREMENTS:

- A bachelor's or master's degree in computer science, information systems, data management, or a related field. Additional certifications or specialized training in data architecture, data management, or data science experience may be advantageous.

- Good understanding of concepts such as data integrity, data modelling, ETL/ELT, data flow diagrams, BPMN, knowledge graphs, triple stores, ontology development & data Science

- Experience with scientific informatics solutions, Life Sciences industry, bio ontologies

- Background in chemistry or biology Science

- Experience with change management aspects of technology transformation with large organizations

- Proven experience designing and developing data architecture frameworks and strategies, preferably in a research-intensive environment or the biotech industry. Experience with data modeling, data integration, and data governance is essential.

- Familiarity with the Pharma Research & Development processes, data needs, and challenges specific to the biotech industry. Understanding of biological data types, such as genomics, proteomics, and clinical data, is highly beneficial.

- Strong knowledge of data management principles, including data quality, data integration, data security, and data privacy. Proficiency in selecting and implementing data management tools, technologies, and platforms is important.

- Proficiency in database systems, data modeling languages (e.g., SQL, NoSQL), and data integration techniques. Experience with data analytics tools, programming languages (e.g., Python, R), and statistical analysis is desirable.

- Excellent communication skills to effectively collaborate with cross-functional teams, researchers, scientists, and IT professionals. Ability to translate complex technical concepts into understandable terms.

- Strong problem-solving and analytical skills to assess data requirements, identify gaps, and develop effective data architecture solutions. Ability to think critically and apply logical reasoning in data-related challenges.

- Familiarity with data governance regulations, privacy laws (e.g., GDPR, HIPAA), and industry standards in data management and security relevant to the biotech field.

- Proactive attitude towards staying updated with emerging trends, technologies, and advancements in data architecture, data management, and bioinformatics. Willingness to learn and adapt to new tools and methodologies.

- Actively engage in the external pharma and animal health science and technology ecosystem in seeking out advances and modern approaches for advanced analytics in the R & D space.