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Tier-1 brand, mid-level experience band, metro context and broad required skillset drive high competition.
Core data engineering skills transfer across industries but require specific platform and cloud experience.
Explicit 4–6 year requirement plus mandatory cloud, Spark, Databricks, and platform skills create strict filters.
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Design, build, test, deploy, and operate data engineering modules, pipelines, and products across cloud, hybrid, and on-premise environments.
Deliver end-to-end data engineering solutions including ingestion, transformation, data modeling, orchestration, serving, and productionization for analytics and AI use cases.
Implement GenAI and Agentic AI workflows including retrieval, tool-calling, orchestration, evaluation, monitoring, and production deployment for enterprise scenarios while collaborating directly with clients and consulting teams.
Bachelor's or Master's degree in Computer Science, Engineering, Technology, Data Science, or related technical field.
4–6 years of relevant experience in data engineering, data platforms, analytics engineering, or enterprise AI/data solution delivery.
Proficiency in advanced SQL and at least one programming language: Python, Java, or Scala.
Hands-on experience with distributed data systems and cloud data platforms (AWS, Azure, or GCP) and tools such as Spark, dbt, Databricks, Snowflake, Hive, Hadoop, Airflow, GitHub Actions, Docker, or Kubernetes.
Experienced in delivering scalable, maintainable, and production-ready data engineering and AI solutions in client-facing consulting environments.
Strong ability to communicate trade-offs, architectural decisions, and risks concisely to business and technical stakeholders.
Demonstrated proficiency with GenAI and Agentic AI implementations and orchestration patterns, plus practical knowledge of data governance, security, and DevOps practices.