Description:

HP is hiring a Data Engineer within its Supply Chain Data & IT organization in Bangalore. This role sits at the intersection of logistics, operations technology, and advanced data architecture—responsible for building and securing performant data processing systems that power supply chain forecasting and machine learning modules. Moving beyond traditional data warehousing, this position focuses on building self-healing pipeline integrations. You will write automated "auto-detect, auto-diagnose, and auto-correct" modules to intercept and remediate data quality anomalies instantly, manage testing protocols, and lead sub-project teams to ensure secure data flow from worldwide factory floors to enterprise analytics engines.

Key Technologies:

SQL, Python, Scala, Java, AWS, Microsoft Azure, Apache Spark, Apache Kafka, Apache Hadoop, Big Data, Data Warehousing, ETL, Machine Learning

Requirements:

• Four-year Bachelor's or Graduate Degree in Computer Science, Information Technology, Software Engineering, Statistics, Mathematics, or a matching quantitative technical stream.
• 4–7 years of professional experience operating within data analytics, data modeling, or data engineering roles (or 3–5 years paired with an advanced Master's/Ph.D. degree).
• Hands-on experience architecting data extraction patterns inside high-volume distributed open-source processing frameworks such as Apache Spark, Apache Hadoop, and handling complex Big Data arrays.
• Direct experience routing high-velocity log architectures, message queuing, and managing stream-processing components utilizing Apache Kafka.
• Practical capability to design, maintain, and secure pipelines across public cloud landscapes, with specific operational depth inside Amazon Web Services (AWS) and Microsoft Azure.
• High technical command writing scalable pipeline code across multiple enterprise languages, specifically Python, Scala, Java, and building complex relational models using advanced SQL.
• Deep knowledge of Data Warehousing design and Extract-Transform-Load (ETL/ELT) schema designs. Proven ability to create automated verification tools for cleaning, validation scrubbing, and complex data pattern trend analysis.
• Conceptual or hands-on capability to program data integrity triggers that dynamically implement auto-detection and auto-correction routines when system data anomalies emerge.
• Experience acting as a technical leader or project owner—directing scrum tasks, formatting detailed test protocols, ensuring QA compliance, and mentoring junior engineers.
• Holding professional programming language certifications (Python/SQL) and possessing active background experience implementing AI/Machine Learning models to solve data quality drift issues.
• Mastery of Agile/Scrum delivery models, high results-orientation, adaptive learning agility, digital fluency, and strong communication skills to drive validations across cross-functional enterprise divisions.