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Tier-1 brand, mid-level generalist title, metro locations, and broad skill list increase candidate competition.
Core data engineering skills are transferable but supply-chain domain specifics raise moderate sensitivity.
Explicit 4-7 years requirement plus domain-specific data engineering tech stack increases filtering strictness.
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Own and enhance secure, high-performance data architectures and pipelines focused on structured and unstructured supply chain data.
Lead data engineering projects including coding, integration, testing, debugging, and mentoring juniors to deliver reliable and cost-effective data solutions.
Analyze data quality issues, perform root cause analysis, and implement automated corrections and improvements to ensure data integrity for machine learning models and business insights.
Bachelor's or Graduate Degree in Computer Science, IT, Software Engineering, Statistics/Mathematics or related discipline or equivalent experience.
4-7 years of relevant experience in data engineering, data analytics, or data modeling (or 3-5 years with advanced degree).
Proficiency in SQL, Python, and Big Data technologies is required; experience in AI solutions for data quality preferred.
Knowledge of Agile methodology and cloud platforms like AWS or Microsoft Azure is expected.
Experienced in end-to-end development of complex data engineering solutions within supply chain or related data domains.
Demonstrated leadership capabilities managing data engineering teams and cross-functional project collaboration.
Strong expertise in data pipeline architecture, data quality automation, and working knowledge of big data technologies such as Apache Spark, Kafka, and Hadoop.