Back
Tier-1 brand, metro Bengaluru location, and broad technical requirements increase candidate competition.
Core data engineering skills transfer across industries, though pharma domain knowledge is beneficial.
Many mandatory senior-level data engineering, DevSecOps, and AI skills increase shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and maintain scalable data architectures and pipelines supporting ETL and business objectives.
Lead design and development of AI agents, skills, and Model Context Protocols (MCPs) including defining and enforcing AI guardrails for secure and scalable solutions.
Provide technical leadership and mentorship across teams on data architecture, platform components, and engineering standards to enable enterprise-wide data initiatives.
Expertise in scalable data architecture design and Big Data technologies such as Spark and Hadoop.
Proficiency in SQL, programming languages like Python, Scala, Java, or C++, and DevSecOps practices including CI/CD.
Experience with cloud platform deployments and tools such as Kubernetes.
Work Experience Required: Not explicitly mentioned in the JD.
Has a strong background in data engineering with end-to-end ownership of data pipelines and platform architecture in large-scale environments.
Experienced in building and operationalizing AI solutions with understanding of Agentic AI capabilities such as planning, decision-making, and autonomous task execution.
Capable of cross-team technical leadership, mentoring, and driving adoption of best practices and standards in a complex, enterprise data ecosystem.