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Tier-1 brand, metro location, and popular data role increase competition despite some niche platform requirements.
Skills like data pipelines, Spark, Kubernetes and Databricks are broadly transferable across industries.
Multiple mandatory technical skills (Kubernetes, Spark, ETL, Python) increase screening rigor.
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Operate and manage Kubernetes clusters in production ensuring high availability, performance, and reliability.
Build, maintain, and enhance large-scale data pipelines and ETL processes supporting analytics and AI/ML use cases.
Develop and maintain monitoring, visualization (Grafana/Power BI), and centralized logging solutions to support data-driven decision-making.
Proven experience operating Kubernetes in production environments.
Hands-on experience with Python for data analysis and development.
Experience with ETL tools, data pipelines, and big data processing frameworks such as Apache Spark.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing containerized workloads and Kubernetes architecture in production settings.
Skilled in end-to-end data engineering including pipeline development, monitoring, and dashboarding with tools like Grafana.
Familiarity with AI/ML model lifecycle support and automotive domain use cases preferred but not mandatory.