As a Lead Data Engineer, you will define and drive the enterprise data engineering strategy for Nike’s next-generation unified analytics foundation spanning Digital, Stores, and Marketplace channels.

This role owns the end‑to‑end data architecture roadmap, including the complete divestiture of Snowflake and successful transition to a Databricks/Spark Lakehouse ecosystem on AWS, while ensuring ≥95% KPI alignment and metric consistency across the enterprise.

You will operate as both a hands-on technical leader and a strategic architect, influencing platform design decisions, governance models, and modernization programs at global scale.

Key Responsibilities:

Architecture & Technical Leadership

Define the target-state data architecture for Nike’s unified analytics platform using Databricks, Spark, and AWS-native services.

Own and execute the Snowflake divestiture strategy, ensuring zero residual footprint and seamless continuity of business reporting.

Lead the design of highly scalable, secure, and cost-efficient data pipelines across batch and streaming workloads.

Establish architectural standards for data modeling, storage formats, and performance optimization.

Data Engineering & Platform Strategy

Design and implement ETL/ELT pipelines using Python, Spark, and SQL, enabling large-scale data transformation and advanced analytics.

Build pipelines leveraging AWS S3, Lambda, EMR, and Databricks, optimized for reliability and performance.

Enable real-time and near-real-time data processing using Kafka, Kinesis, and Spark Streaming.

Drive containerized deployment strategies using Docker and Kubernetes.

Orchestration, CI/CD & Infrastructure

Lead global orchestration standards using Apache Airflow for complex, cross-domain workflows.

Implement CI/CD pipelines using Git, Jenkins, and enforce best practices for quality, security, and automation.

Own infrastructure provisioning through Infrastructure as Code (Terraform / CloudFormation).

Data Governance & Enterprise Metrics

Establish and govern enterprise-wide data lineage, cataloging, and access control using Unity Catalog and metadata-driven designs.

Define and manage metric dictionaries and KPI frameworks, ensuring semantic consistency across domains.

Partner with analytics, product, and business teams to drive ≥95% KPI alignment and trusted insights

Observability & Operational Excellence

Implement robust monitoring, alerting, and observability across pipelines and platforms.

Define SLAs, SLOs, and operational playbooks to support mission-critical analytics workloads.

Mentor and technically guide senior and mid-level engineers, raising the overall engineering bar.

Must-Have Qualifications

6 to 8+ years of experience in data engineering, distributed systems, and platform architecture with clear technical ownership.

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

Advanced Python for data processing, automation, testing, and optimization.

Advanced SQL expertise for complex querying, windowing functions, data modeling, and performance tuning.

Demonstrated success in modernizing legacy platforms and migrating complex analytics logic to Databricks/Spark Lakehouse architectures.

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

Certifications (Mandatory):

Databricks Certified Data Engineer – Professional ( Mandatory)

AWS Solutions Architect – Associate or Professional (preferred)