At the center of Nike's Digital Transformation is our Consumer Direct Offense. Nike is driving investments in high quality data, platforms, and models to create unbreakable relationships with consumers through state-of-the-art personalization and analytics. Nike is looking for a seasoned engineering leader who will evangelize and lead privacy within our consumer data engineering organization, lead data quality and data governance efforts to ensure Nike has the highest quality consumer data asset and implement a taxonomy strategy so Nike can fully leverage and classify our digital assets. You will develop cross-functional teams of instrumentation engineers, data engineers, software engineers and analytics developers to deliver solutions at massive scale. These include partnering with our Consumer Data Engineering and Analytics team, Consumer Direct Sciences, Data Governance, and Privacy teams to solve complex business problems to deliver brand-new consumer experiences.
What you will do:
- Develop a vision and long-term engineering roadmap for enterprise data management including taxonomy, data governance, privacy compliance, data quality, and master data management for Nike's consumer data asset.
- Build and grow highly skilled, cross-functional product teams that deliver end-to-end foundational capabilities related to privacy, data quality, data governance, taxonomy (e.g. Compliance Utility, Compliance Runs, Regression Testing Framework, User Blacklists)
- Manage the professional development and career plans for those on your teams, help us develop the technology leaders of tomorrow.
- Partner with data quality teams and lead improvement efforts in regression and unit testing, continuously improve and enhance pipelines, partner with engineering teams and drive high quality end to end solutions.
- Lead engineering taxonomy initiatives to develop enterprise data model to help classify, tag, and leverage Nike's digital assets for personalization.
- Implement scalable data cataloging and lineage solution to improve visibility of consumer data lake assets.
- Set high standards for engineering excellence and utilize engineering best practices to consistently deliver scalable, production-grade solutions while actively managing costs and overhead. Continually raise the bar to improve standards, quality, reliability, and velocity of the teams.
- Leverage your prior experience, knowledge of industry trends, and personal creativity to develop new and innovative solutions which delight our customers. Given the rapid pace of change in technology and data systems today, always be pushing the boundary of what's possible and be on the offense always.
- Embrace and embody Nike's core values (maxims) in your work and interactions with peers, stakeholders, and direct reports.
- Role model transparency and accountability as a leader of Nike. Communicate effectively, build trust and strong relationships across the company, do the right thing.
- Engage employees meaningfully, assist the teams by removing roadblocks, align efforts across Nike's matrix to drive progress forward and always win as a team.
Who you are:
- A demonstrated technical leader with deep knowledge and experience in creating production grade, end-to-end data services at scale. You are comfortable discussing modern data architectures (e.g. Lambda vs. Kappa), distributed databases (HBase, Cassandra, Dynamo) and CAP theorem. Able to dive into code and architecture to provide an informed opinion on benefits/trade-offs of different design choices.
- Broad understanding of major privacy, legal and regulatory requirements including GDPR, CCPA and DPA.
- Strong background with identity and Access Management and understand the importance of quality data to better manage access to enterprise data and resources.
- Strong data quality background with experience developing operational processes and technologies and leveraging change management to improve data accuracy.
- Proven track record of leading data classification or taxonomy initiatives.
- Highly organized and able to handle multiple timelines, priorities, and teams with ease. At the same time, able to quickly flex and pivot due to changing circumstances which present new opportunities for Nike.
- An effective communicator who can seamlessly transition between discussing high level vision with executives to low level tactics with the dev team. You are able to influence people and align efforts across the company to act as one in pursuit of a common goal.
- A constant learner who is passionate about staying at the forefront of the engineering and software landscape through reading journals, books, or blogs. In equal measure, someone who is passionate about growing people and understanding how to effectively lead an organization.
- Experienced with the capture, schema validation, instrumentation and provisioning of consumer data to ensure Nike has the highest quality consumer data to drive our consumer direct offense at scale.
- Bachelor's Degree in computer science, software engineering, or related field (preferably Masters or Ph.D. preferred) and 8+ years of experience in developing production grade code, preferably related to consumer data products and analytics.
- Deep knowledge of software architecture and engineering best practices, especially modern cloud computing stacks for processing big data and deploying microservices at scale. For example, AWS for cloud provider, EKS for Kubernetes, Docker for containers, Jenkins for CI/CD, Kafka, Spark, Nifi, etc.
- Strong background in data quality and data governance related to consumer analytics.
- Experience influencing platform development for providing foundational stream processing, data engineering, data quality, data governance, data access, and data serving. (i.e. Kafka Streams, Flink, Spark, Presto, Dynamo, Snowflake, Apache Ranger, Druid, HBase, etc.)
- Experience integrating engineering operations into a DevOps or DataOps model and CICD principles.
- Experience automating data cataloging, lineage and discovery via Collibra and enable API integration with abovementioned data stores and workflow tools.