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Tier-1 brand, metro location, and broad managerial data skill requirements increase competition.
Core data engineering and platform skills are transferable, though retail-specific product knowledge may be beneficial.
Explicit 10+ years, 3+ leadership years, and specific tech/platform requirements create strict filtering.
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Lead and manage a data engineering team to build and operate scalable, fault-tolerant enterprise data platforms including lakehouse, data warehouse, ingestion, transformation, and integration capabilities.
Own strategy, architecture, and technical roadmap aligning with product, architecture, and business goals for data platform capabilities that enable analytics, reporting, AI/ML, and business decision-making.
Manage delivery cadence, resource planning, stakeholder collaboration, and enforce engineering best practices including automation, testing, monitoring, documentation, and CI/CD.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics or related technical field or equivalent experience.
10+ years of hands-on experience in data engineering with production-grade pipelines and platform development at scale.
3+ years of people leadership experience including hiring, coaching, and performance management of technical teams.
Strong proficiency with SQL, Python, distributed data processing (Spark/PySpark), cloud data platforms (AWS, Snowflake, Databricks), and orchestration/streaming tools (Airflow, dbt, Kafka, Kinesis).
Experienced leader capable of managing globally distributed teams in complex, large-scale data platform environments.
Technically deep in modern cloud-native data architectures and able to align engineering with cross-functional business and product stakeholders.
Skilled in driving platform reliability, data governance standards, AI/ML-ready data products, and continuous improvement through DevOps/DataOps practices.