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Metro location and broad tech stack increase competition, but seniority and niche skills limit applicant pool.
Core data engineering skills are broadly transferable, though storage and AI integration specifics need adjustment.
Explicit 8+ years and mandatory Big Data, Spark, Kafka, Kubernetes skills enforce strict filters.
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Architect and lead end-to-end data analytics and AI/automation solutions across teams and stakeholders.
Design, build, and support batch and streaming data pipelines, customer analytics platforms, and AI assistants using Java/Python.
Develop automation services, REST APIs, backend integrations, and deploy applications on Kubernetes/Edge with GitOps and CI/CD.
Bachelor’s or Master’s degree in Computer Science or Software Engineering.
Minimum 8+ years of relevant work experience.
Proficiency in Java and/or Python programming, and strong computer science fundamentals.
Experience with database technologies (SQL/NoSQL), big data tools (Hadoop, Hive), and familiarity with agentic AI concepts (tool usage, orchestration, MCP).
Experienced data engineering leader with hands-on skills in architecting large-scale batch and streaming pipelines.
Strong background integrating AI systems with enterprise tools, familiar with agentic AI frameworks and model context protocols.
Comfortable working with cross-functional teams to productize solutions and overcoming technical challenges in cloud and edge environments.