Lead Product Analyst

Job Description: Lead Product Analyst – Manufacturing & Engineering Data & Analytics Team

Company: GSK

Location: [Location]

As a Lead Product Analyst within the Manufacturing & Engineering Data & Analytics Team at GSK, your role will have a direct impact on the achievement of Supply Chain Digital & Tech (SCD&T) objectives. We are looking for someone who is at the forefront of technology and can help our Data and Analytics Team embrace and adopt modern ways of data management, ingestion, and providing analytics to support our Manufacturing and Engineering Business Teams.

Key Responsibilities:

1. Strong engineering and technology background with the ability to learn quickly and go deep into our product solutions.
2. 2. DevOps mindset and experience of working in an agile way.
3. 3. Excellent listener and proven collaborator with leaders and peers.
4. 4. Ability to build strong partnerships with other technology teams to stay on the leading edge of industry solutions.
5. 5. Hands-on, ‘roll up your sleeves’ collaborative style of working.
6. 6. Bring energy and enthusiasm to the job and organization.
7. 7. Development, maintenance, and management of data products and solutions, including identifying gaps, risks, and mitigation approaches.
8. 8. Consistently attain/exceed individual goals.
9. 9. Drive transformation and contribute to platform designs and development aspects, focusing on delivering end-to-end solutions and building reusable frameworks and utilities.

Specific Responsibilities:

1. Partner with the Product Manager(s) to develop, test, deploy, and maintain data products and/or solutions using an Agile Product Model and DevOps principles.
2. 2. Enable continuous integration and delivery practices leveraging pipelines and automation.
3. 3. Enable self-service, X-as-a-service app developer platform services for cloud applications.
4. 4. Look for opportunities to modernize existing tech stacks and data platforms.
5. 5. Strong knowledge of data architecture implemented in the cloud.
6. 6. Strong knowledge of continuous integration and continuous delivery (CICD) pipelines.
7. 7. Contribute to the delivery of an enterprise data lake in support of analytics, reporting, and data science.
8. 8. Deliver data products in alignment with data processes, including ingestion, curation, data governance, data quality, data cataloging, and CI/CD.
9. 9. Support the implementation of solutions, choosing the right technologies, and evaluating the evolution of the architecture as business needs change.
10. 10. Stay current with emerging technology trends and demonstrate vast knowledge of tools and technologies within the industry applicable to building data products for Manufacturing and Engineering.
11. 11. Demonstrate a combination of business focus, strong analytical and problem-solving skills, and programming knowledge to quickly cycle hypothesis through the discovery phase of the project.
12. 12. Excellent written and communication skills to report findings in a clear, structured manner.

Requirements:

1. Bachelor’s degree in engineering or sciences or equivalent relevant experience.
2. 2. Experience in building and managing data and analytics applications.
3. 3. Good communication skills.
4. 4. Demonstrated ability to solve complex problems and deliver significant process improvements.
5. 5. Self-motivated, pragmatic, resourceful, and acts without waiting to be told what to do.
6. 6. Proficient in building, deploying, and scaling applications using cloud-based Data Lake technologies.
7. 7. Experience with modern data engineering tools (e.g., Talend, Spark, Snowflake).
8. 8. Knowledge of data ingestion, curation, and quality tools and processes.
9. 9. Experience in ETL/Data Integration tools (e.g., Talend, Python, Pyspark).
10. 10. Agile, DevOps & Automation of testing, build, deployment, CI/CD, etc.
11. 11. Knowledge of product management.
12. 12. Knowledge of Cloud-Based Big Data platforms to design and implement scalable solutions.
13. 13. Experience and knowledge in AI/ML projects development and implementation with a basic understanding of ML concepts.
14. 14. Knowledge of Data Governance and Quality standards to support the implementation of robust data quality and governance standards.
15. 15. Knowledge of agile delivery.
16. 16. Experience in working with different integration architectures for identity, data, or infrastructure and cloud.

Preferred Qualifications:

If you have the following characteristics, it would be a plus:

1. Experience with data structures, data models, or relational database design.
2. 2. Subject matter expertise with relevant business processes.

If you have the skills and qualifications mentioned above, we would like to speak to you.