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Metro location, generalist data engineer title, and broad technology requirements increase candidate competition.
Medium because core data engineering skills transfer across industries, though retail/supply-chain domain experience is preferred.
High due to explicit 12+ years requirement, multiple mandatory technologies, and leadership/mentorship expectations.
Lead a track within Supply Chain & Planning Data Domain to deliver end-to-end data solutions including requirements analysis, design, development, testing, deployment, and production support.
Architect and develop scalable batch and real-time data pipelines, data models, transformations, and data products leveraging event-driven and streaming technologies like Kafka, Google Pub/Sub, and Spark Streaming.
Provide technical leadership, enforce data quality governance, troubleshoot data issues, and collaborate with cross-functional and global teams including analysts, data scientists, and engineers to deliver reliable, scalable, and cost-efficient data products.
12+ years of experience in Data Engineering, Data Warehousing, or related disciplines with large-scale data solutions delivery.
Proficiency in SQL and Python for large-scale data processing and transformation; hands-on with big data technologies such as Spark, Flink, Hive.
Experience designing and implementing event-driven architectures and real-time streaming data pipelines using Kafka, Google Pub/Sub, Spark Streaming or similar.
Ability to work with global teams with overlap in U.S. time zones; B.E. in Computer Science is good to have but not mandatory.
Experienced in cloud-native modern data platforms, preferably with exposure to Google Cloud Platform and data transformation frameworks like dbt, PySpark, or Dataflow.
Strong technical leadership skills demonstrated via mentorship and leading complex data pipelines and product delivery in a cross-continental environment.
Familiar with Agile delivery, CI/CD, Infrastructure as Code, DevOps, and committed to engineering best practices to improve platform scalability, stability, and cost efficiency.