ROLE OVERVIEW :

The Lead Data Engineer will define and drive the enterprise data engineering strategy for Nike's next-generation unified analytics foundation spanning Digital, Stores, and Marketplace channels. The role owns the end-to-end data architecture roadmap, including complete divestiture of Snowflake and transition to a Databricks/Spark Lakehouse ecosystem on AWS, ensuring at least 95% KPI alignment and metric consistency across the enterprise. The position requires both hands-on technical leadership and strategic architecture expertise at a global scale.

KEY RESPONSIBILITIES :

- Architecture and Technical Leadership : Define the target-state data architecture using Databricks, Spark, and AWS-native services. Own and execute the Snowflake divestiture strategy ensuring zero residual footprint and seamless reporting continuity. Design scalable, secure, and cost-efficient data pipelines for batch and streaming workloads. Establish architectural standards for data modeling, storage formats, and performance optimization.

- Data Engineering and Platform Strategy: Design and implement ETL/ELT pipelines using Python, Spark, and SQL. Build and optimize pipelines leveraging AWS S3, Lambda, EMR, and Databricks. Enable real-time and near real-time processing using Kafka, Kinesis, and Spark Streaming. Drive containerized deployments using Docker and Kubernetes.

- Orchestration, CI/CD and Infrastructure: Lead orchestration standards using Apache Airflow for complex workflows. Implement CI/CD pipelines using Git and Jenkins while enforcing automation and security best practices. Own infrastructure provisioning through Infrastructure as Code using Terraform or CloudFormation.

- Data Governance and Enterprise Metrics: Establish enterprise-wide data lineage, cataloging, and access control using Unity Catalog. Define and manage metric dictionaries and KPI frameworks ensuring semantic consistency. Collaborate with analytics, product, and business teams to drive trusted insights and KPI alignment.

- Observability and Operational Excellence: Implement monitoring, alerting, and observability across data platforms. Define SLAs, SLOs, and operational playbooks for mission-critical workloads. Mentor and guide senior and mid-level engineers to elevate engineering standards.

MANDATORY QUALIFICATIONS :

- Minimum 10 years of experience in data engineering, distributed systems, and platform architecture with clear ownership.

- Deep AWS expertise including S3, Lambda, EMR, and Databricks in large-scale production environments.

- Advanced proficiency in Python for data processing, automation, and optimization.

- Advanced SQL expertise including complex querying, window functions, data modeling, and performance tuning.

- Demonstrated experience in modernizing legacy platforms and migrating analytics logic to Databricks/Spark Lakehouse architecture.

- Strong experience in data governance, lineage, cataloging, and enterprise metric management.

MANDATORY CERTIFICATIONS :

- Databricks Certified Data Engineer - Professional

- AWS Solutions Architect - Associate or Professional preferred