At a Glance
- Tasks: Lead hands-on data engineering processes and manage complex data systems in a dynamic environment.
- Company: Join Pfizer, a global leader in healthcare innovation and security.
- Benefits: Flexible office presence, competitive salary, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on continuous improvement and innovation.
- Why this job: Make a real impact on data reliability and efficiency in a cutting-edge analytics ecosystem.
- Qualifications: Bachelor's degree in Computer Science or related field; strong data engineering experience required.
The predicted salary is between 60000 - 80000 £ per year.
The Enterprise Platforms & Security (EP&S) organization delivers capabilities for Pfizer, including business application platforms supporting enterprise applications and critical business processes, infrastructure for business traffic, and security measures for important information assets. The Manager Data Engineering Lead is responsible for the development and technical management of the Data Engineering process within the Enterprise Platform and Security Analytics function. This hands-on role supports a large-scale analytics ecosystem powered by AWS and Splunk, focusing on data reliability, efficiency, and quality.
ROLE RESPONSIBILITIES
- Design, manage, and troubleshoot complex large-scale data engineering methods within hybrid on-premise and cloud-hosted environments.
- Utilize vendor apps and develop custom app configurations to standardize and normalize diverse data source types.
- Develop and maintain service patterns used for the data engineering process and data collection methods.
- Partner with internal and external teams to implement solutions that improve data engineering processes and enable automated and/or self-service data onboarding.
- Collaborate with the Service Manager, Data Stewards, and customers to support and prioritize business requirements.
- Support the Data Management Lifecycle engineering processes from inception and design through deployment, operation, and optimization.
- Ensure documentation details the methods to collect, triage, and backfill data feeds to support monitoring and data resiliency.
- Proactively assess and identify opportunities to better maintain and reduce ingestion volume.
- Manage technical work activities of contingent worker resources to engineer data and ensure adherence to procedures and standards.
- Ensure high data reliability, efficiency, and quality standards are maintained and continuously improve engineering practices and processes.
- Support 24x7 oversight of Business as Usual (BAU) operations, monitor service quality levels, and respond to outages or performance issues with urgency.
- Participate in incident, problem, and change management processes.
QUALIFICATIONS
- Bachelor's degree in Computer Science or physical sciences; Master's degree preferred.
- Robust experience in a hands-on data engineering role.
- Flexible to changing priorities and comfortable in a fast-paced dynamic environment.
- Superior analytical and creative problem-solving skills.
- Strong problem-solving abilities with an analytic and qualitative eye for reasoning under pressure.
- Self-starter with the ability to independently prioritize and complete multiple tasks with little to no supervision.
- Experience developing software code in one or more programming languages (Java, JavaScript, Python, etc.).
- Experience in designing, developing, and implementing advanced data collection methods, sizing for data storage, index strategies, ingesting/indexing processes, transforming/normalizing data, and data enrichment/anonymization upon ingest.
- Experience with Unix/Linux operating system.
PREFERRED QUALIFICATIONS
- Robust hands-on experience in implementation and performance tuning of Kinesis, Kafka, Spark, or similar implementations.
- Strong familiarity with data engineering in Splunk.
- Experience implementing AWS automation services and process builds to create syslog and HEC ingestion into AWS for processing and optimized data flow.
- Extensive experience with design, development, and operations leveraging deep knowledge in services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon Sagemaker, Amazon EMR, NoSQL technologies.
- Experience with relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis).
- Experience transforming large datasets into consumable assets for self-service analytics and reporting.
- Familiarity with machine learning concepts.
Manager, Data Engineering Lead employer: Pfizer Inc.
Pfizer is an exceptional employer that fosters a dynamic and collaborative work culture, particularly for the Manager, Data Engineering Lead role based in Sandwich, Kent. With a strong emphasis on employee growth and development, Pfizer offers opportunities to work with cutting-edge technologies in a supportive environment that values innovation and teamwork. The flexible office presence policy further enhances work-life balance, making it an attractive place for motivated individuals seeking meaningful and rewarding careers in data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Manager, Data Engineering Lead
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your data engineering projects. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to data engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Manager, Data Engineering Lead
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Manager, Data Engineering Lead. Highlight your hands-on experience in data engineering and any relevant projects you've worked on that align with our needs at Pfizer.
Showcase Your Skills:Don’t just list your skills; demonstrate them! Use specific examples from your past work to show how you’ve tackled complex data engineering challenges, especially in AWS or Splunk environments.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Explain why you're passionate about data engineering and how your background makes you a perfect fit for our team. Keep it engaging and personal!
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Pfizer Inc.
✨Know Your Data Engineering Stuff
Make sure you brush up on your data engineering skills, especially around AWS and Splunk. Be ready to discuss specific projects where you've designed or managed large-scale data processes, as this will show your hands-on experience.
✨Show Off Your Problem-Solving Skills
Prepare examples of how you've tackled complex data challenges in the past. Think about times when you had to troubleshoot issues or improve data reliability and efficiency. This will demonstrate your analytical mindset and ability to work under pressure.
✨Collaborate Like a Pro
Since this role involves working with various teams, be ready to talk about your collaboration experiences. Highlight instances where you partnered with others to implement solutions or improve processes, showcasing your team-oriented attitude.
✨Be Ready for Technical Questions
Expect some technical questions related to programming languages like Java, Python, or any relevant tools like Kafka or Spark. Brush up on these topics and be prepared to explain your thought process when developing data collection methods or optimising data flows.
