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Mid-level, popular data engineer role in Pune with multiple generalist amplifiers increases applicant competition.
Data engineering skills are broadly transferable, with some domain-specific financial data preference.
Explicit 7–10 years plus mandatory Airflow, AKS, Python, and LLM production experience increases strictness.
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Design, develop, and maintain scalable ETL workflows and data pipelines supporting critical business applications and analytics.
Build and manage data orchestration workflows using Apache Airflow and deploy containerized applications on Azure Kubernetes Service (AKS).
Integrate and support generative AI solutions (OpenAI, Anthropic Claude) and maintain CI/CD pipelines leveraging GitHub Actions.
7–10 years of experience in data engineering, software engineering, or related technical disciplines.
Bachelor’s or master’s degree in computer science, Engineering, Information Technology, or a related field.
Strong hands-on expertise with Python, GitHub, GitHub Actions CI/CD, Azure Kubernetes Service (AKS), Apache Airflow, Docker, and building enterprise-scale ETL/data pipelines.
Experience with generative AI models (OpenAI, Claude) in production and cloud-native, distributed data platform design.
Senior-level professional with deep expertise in scalable data engineering, cloud-native architectures, and DevOps automation.
Experience operating in enterprise environments requiring collaboration with data scientists, ML engineers, and business stakeholders.
Proficient in managing containerized workloads, CI/CD automation, and integrating generative AI capabilities into data platforms.