Manager, Data Engineering Lead
Location
Sandwich, Kent | United Kingdom
Job description
ROLE SUMMARY
The Enterprise Platforms & Security (EP&S) organization delivers the following capabilities for Pfizer. Business application platforms supporting Pfizer's enterprise application and critical business processes. Infrastructure allowing business traffic to travel where it needs to go, internally and externally, along with the appropriate access controls. EP&S secures Pfizer's most important information assets through world class controls and protections and enables Pfizer's business results by making security an enabler and not a roadblock to achieving business results.
The Manager Data Engineering Lead is responsible for the development and end-to-end technical hands-on management of the Data Engineering process within the Enterprise Platform and Security Analytics function. This is a hands-on data engineering role which primarily supports our large-scale analytics ecosystem powered by AWS and Splunk. Its scope includes a wide range of highly transactional platforms and cybersecurity data sources with diverse collection points and complicated collection methods. Ensuring data reliability, efficiency, and quality is core to this role.
For Pfizer, we look for candidates that are motivated, self-learning, and team-oriented individuals. From a technical perspective, an ideal candidate would have the skills shown below, but candidates that possess a strong subset and an attitude towards self-development and growth will be considered.
ROLE RESPONSIBILITIES - Design, manage, and troubleshoot complex large-scale data engineering methods within a 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 which 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, continuously assess and identify opportunities to better maintain and reduce ingestion volume.
- Manage technical work activities of contingent worker resources to engineer data and ensure procedures and standards are adhered to.
- 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, along with continual monitoring of the service for quality levels, and response to outages or performance issues with a sense of 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-passed dynamic environment.
- Superior analytical and creative problem-solving skills. Demonstrate successes in analysis, conclusions, and improvement.
- 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 to common standards, data enrichment and/or anonymization of data upon ingest.
- Can showcase 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 build to create syslog and HEC ingestion into AWS for processing and optimized data flow.
- Extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon Sagemaker, Amazon EMR, NoSQL technologies
- Experience with one or more relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis)
- Experience transforming large datasets into consumable assets for self-service analytics and reporting.
- Familiar with Machine learning concepts.
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