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Tier-1 brand, mid-level generalist data role, metro locations, and broad skillset increase applicant competition.
Core data engineering skills are transferable across industries, though supply-chain domain knowledge is beneficial.
Explicit 4–7 year requirement plus broad mandatory data engineering tech stack increases filtering strictness.
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Own data collection procedures and ensure data integrity for machine learning models by performing data cleansing, validation, and root-cause analysis with corrective automation.
Design, develop, and maintain secure, performant data architectures and pipelines for structured and unstructured data, including testing, debugging, and integration.
Lead and mentor a team of data engineers, collaborate cross-functionally to define reporting deliverables, and represent the data engineering team in complex projects.
Bachelor’s or Graduate Degree in Computer Science, IT, Software Engineering, Statistics/Mathematics, or related discipline, or equivalent experience.
4-7 years of work experience in data analytics, data engineering, or data modeling (or 3-5 years with an advanced degree).
Proficiency in programming languages such as SQL, Python; experience with big data technologies (AWS, Hadoop, Kafka, Spark) as required to build data pipelines and implement AI solutions.
Work Experience Required: 4-7 years or 3-5 years with advanced degree. Notice Period: Not explicitly mentioned in the JD.
Experienced data engineer with demonstrated ability to lead teams on complex, large-scale data pipeline and architecture projects within supply chain or related fields.
Strong technical expertise in big data ecosystems and AI-driven data quality automation, with skills in multiple programming languages and cloud platforms.
Operates well in project leadership roles requiring coordination across multiple teams and delivering reliable, scalable data solutions.