At a Glance
- Tasks: Lead a team to build scalable data pipelines and innovative data solutions.
- Company: Join GSK, a global biopharma leader dedicated to improving health.
- Benefits: Competitive salary, bonuses, health insurance, and generous leave policies.
- Other info: Dynamic work culture focused on innovation and professional growth.
- Why this job: Make a real impact on healthcare by leveraging cutting-edge data technology.
- Qualifications: 7+ years in data engineering with strong cloud and programming skills.
At GSK, we want to supercharge our data capability to better understand our patients and accelerate our ability to discover vaccines and medicines. The Onyx Research Data Platform organization represents a major investment by GSK R&D and Digital & Tech, designed to deliver a step-change in our ability to leverage data, knowledge, and prediction to find new medicines.
We are a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and DevOps, data / metadata / knowledge platforms, and AI/ML and analysis platforms, all geared toward:
- Building a next-generation, metadata- and automation-driven data experience for GSK’s scientists, engineers, and decision-makers, increasing productivity and reducing time spent on “data mechanics”
- Providing best-in-class AI/ML and data analysis environments to accelerate our predictive capabilities and attract top-tier talent
- Aggressively engineering our data at scale, as one unified asset, to unlock the value of our unique collection of data and predictions in real-time
Data Engineering is responsible for the design, delivery, support, and maintenance of industrialized automated end-to-end data services and pipelines. They apply standardized data models and mapping to ensure data is accessible for end users in end-to-end user tools through use of APIs. They define and embed best practices and ensure compliance with Quality Management practices and alignment to automated data governance. They also acquire and process internal and external, structured and unstructured data in line with Product requirements.
This role is responsible for building and leading a scrum team of world-class data engineers focused on building automated, scalable, and sustainable pipelines to account for evolving scientific needs. They support the head of Data Engineering in building a strong culture of accountability and ownership in their team, as well as instilling best-in-class engineering practices (e.g., testing, code reviews, DevOps-forward ways of working). They work in close partnership with our Platforms teams to ensure we have the right tools and ways of working, and with our Bioinformatics teams to ensure the use of appropriate schemas, vocabularies, and ontologies.
Key Responsibilities:
- Lead a team of data engineers in delivering data and knowledge products that advance GSK R&D
- Architect of the data delivery and operational strategy for their team, who can deconstruct a complex and ambiguous data or knowledge request into a detailed strategy to make decisions, anticipate future issues, and drive engineering efficiencies.
- Partner closely with other data engineering leads to conceptualize the design of new data flows aimed at maximizing reuse and aligning with an event-driven microservice-enabled architecture.
- Partner with other Data Engineering leads to architect an engagement model and optimal ways of working with the product management teams.
- Able to design innovative strategy beyond the current enterprise way of working to create a better environment for the end users, and able to construct a coordinated, stepwise plan to bring others along with the change curve.
- Standard bearer for proper ways of working and engineering discipline, including the QMS framework and CI/CD best practices and proactively spearhead improvement within their engineering area.
- Exemplar leaders in their field of technical knowledge, keen on bettering their understanding and acting as the knowledge holder for the organization.
Why You?
Basic Qualifications:
- Bachelors degree in Data Engineering, Computer Science, or Software Engineering
- 7+ years of professional experience
- Software engineering experience
- Cloud experience
- Experience in automated testing and design
Preferred Qualifications:
- Masters or PhD
- Strong data engineering experience in industry
- Demonstrable experience overcoming high volume, high compute challenges
- Familiarity with orchestrating tooling
- Experience with DevOps-forward ways of working
- Deep knowledge and use of at least one common programming language: e.g., Python, Scala, Java
- Deep experience with common big data tools (e.g., Spark, Kafka, Storm, ...)
- Cloud experience (e.g., AWS, Google Cloud, Azure, Kubernetes)
- Application experience of CI/CD implementations using git and a common CI/CD stack (e.g., Jenkins, CircleCI, GitLab, Azure DevOps)
- Experience with agile software development environments using tools like Jira and Confluence
The annual base salary for new hires in this position ranges from $182,750 to $247,250 taking into account a number of factors including work location, the candidate’s skills, experience, education level and the market rate for the role. In addition, this position offers an annual bonus and eligibility to participate in our share-based long-term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave.
GSK is a global biopharma company with a special purpose – to unite science, technology and talent to get ahead of disease together – so we can positively impact the health of billions of people and deliver stronger, more sustainable shareholder returns – as an organization where people can thrive. Getting ahead means preventing disease as well as treating it, and we aim to positively impact the health of 2.5 billion people by the end of 2030.
Our success absolutely depends on our people. While getting ahead of disease together is about our ambition for patients and shareholders, it’s also about making GSK a place where people can thrive. We want GSK to be a workplace where everyone can feel a sense of belonging and thrive as set out in our Equal and Inclusive Treatment of Employees policy.
Senior Data Engineer & Team Lead - Scalable Pipelines employer: GlaxoSmithKline
GSK is an exceptional employer that prioritises innovation and collaboration, making it a prime choice for professionals in the data engineering field. With a strong commitment to employee growth, GSK offers comprehensive benefits, a supportive work culture, and opportunities to lead cutting-edge projects that directly impact global health. Located in a vibrant environment, employees are encouraged to thrive both personally and professionally while contributing to meaningful advancements in medicine and technology.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer & Team Lead - Scalable Pipelines
✨Tip Number 1
Network like a pro! Reach out to current employees at GSK or in similar roles on LinkedIn. A friendly chat can give you insider info and might even lead to a referral, which is always a bonus!
✨Tip Number 2
Prepare for the interview by brushing up on your technical skills and understanding of data engineering principles. Be ready to discuss how you've tackled complex data challenges in the past – real examples will make you stand out!
✨Tip Number 3
Show your passion for data! During interviews, express your enthusiasm for using data to drive decisions and improve outcomes. GSK is all about making a positive impact, so let them know you're on board with that mission.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re serious about joining the GSK team and contributing to their amazing work in healthcare.
We think you need these skills to ace Senior Data Engineer & Team Lead - Scalable Pipelines
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior Data Engineer role. Highlight your experience with data pipelines, cloud technologies, and any leadership roles you've held. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our mission at GSK. Be sure to mention specific projects or experiences that relate to the job description.
Showcase Your Technical Skills:Don’t forget to highlight your technical skills in your application. Mention your proficiency in programming languages like Python or Java, and any big data tools you’ve worked with. We love seeing candidates who are hands-on with the tech!
Apply Through Our Website:We encourage you to apply through our website for the best chance of being noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at GlaxoSmithKline
✨Know Your Data Engineering Fundamentals
Brush up on your data engineering principles, especially around automated testing and CI/CD practices. Be ready to discuss how you've applied these in past projects, as GSK values a strong foundation in these areas.
✨Showcase Your Leadership Skills
As a Senior Data Engineer & Team Lead, you'll need to demonstrate your ability to lead a team effectively. Prepare examples of how you've fostered a culture of accountability and ownership in previous roles, and be ready to discuss your approach to mentoring junior engineers.
✨Understand GSK's Mission
Familiarise yourself with GSK's goals, particularly their focus on leveraging data to improve patient outcomes. Be prepared to articulate how your skills and experiences align with their mission to discover new medicines and vaccines.
✨Prepare for Technical Questions
Expect technical questions that assess your knowledge of big data tools like Spark and Kafka, as well as cloud platforms such as AWS or Azure. Practice explaining complex concepts clearly, as you may need to communicate these ideas to non-technical stakeholders.