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PRINCIPAL, DATA ENGINEER
Walmart Inc. (company site, opens in a new tab)12–16 yrsBengaluruOn-siteDirect career pageEst. 55–95 LPA
Posted 6h ago
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Job description
Structured overview of role & requirements
About This Role
- Own design, development, testing, and deployment of high-scale data pipelines and architectures processing terabytes to petabytes, with emphasis on scalability, fault tolerance, accuracy, and performance.
- Drive data solutions from system design through release; troubleshoot data/process issues, improve reliability and performance, and implement monitoring, security, and access controls.
- Partner with product, business, and engineering teams on priorities and tool evaluations; mentor engineers and establish development practices.
Minimum Requirements
- Work Experience Required: JD states an experience range of 12–16 years; it also lists 12 years of hands-on Python, 8+ years in databases/SQL/PLSQL/stored procedures, 7+ years building large-scale data pipelines, and 4 years in data or analytics-related fields. Minimum-qualification alternatives separately list a CS bachelor’s plus 5 years, a CS master’s plus 3 years, or 7 years in software engineering/related work.
- Degree: Bachelor’s or master’s in computer science or a related technical field is listed; minimum qualifications also provide experience-based alternatives without a degree requirement.
- Technical requirements include object-oriented Python, SQL/database fundamentals, and large-scale data engineering with technologies such as Spark, Kafka, Cassandra, Hadoop, Hive, Presto, or Airflow; strong distributed-systems and ETL/data-modeling knowledge is expected.
- Work location: Daily office work in Bangalore or Chennai.
Ideal Candidate Profile
- A senior hands-on data engineer who has designed and operated fault-tolerant, high-volume distributed pipelines and can make trade-offs across reliability, performance, and cost.
- Strong fit for someone experienced across the data lifecycle—from HLD/LLD and implementation to testing, deployment, monitoring, and root-cause analysis—with security and data quality built into delivery.
- Likely to succeed in a large, geographically distributed engineering environment, partnering with product and business teams while mentoring other engineers.