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Tier-1 brand and metro location but senior, specialized data/SRE role reduces applicant density.
Data engineering and SRE skills are transferable, but enterprise data platform depth raises domain sensitivity.
Mandatory 10+ years and deep SRE/data platform expertise produce strict shortlisting filters.
Lead design, development, and delivery of advanced data engineering and enterprise data platforms with a strong focus on reliability, scalability, and operational excellence.
Champion SRE best practices including automated monitoring, alerting, and self-healing to ensure high system availability and resilience.
Drive multi-team adoption of AI-assisted engineering and SDLC automation, establish measurable targets, lead root cause analysis, and mentor teams on reliability and automation.
10+ years of applied software engineering experience with formal training or certification.
Proficiency in Java, Python, and big data technologies such as Spark/PySpark, Databricks, Snowflake.
Demonstrated expertise in implementing SRE principles and operating distributed, cloud-native large-scale data processing systems.
Experience leading multi-team adoption of enterprise AI-assisted development tools and defining governance and quality measures.
Experienced in engineering and architecture of large-scale enterprise data platforms focused on operational stability and security.
Proficient in applying observability tools (Dynatrace, Splunk, Grafana) and incident management in agile environments with CI/CD and application resiliency.
Skilled in coordinating across engineering, product, and operations teams with a strong understanding of responsible AI use and continuous improvement culture.