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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand and Pune metro increase applicant density, offset by senior, specialized platform requirements.
Strong domain and platform-specific requirements make cross-industry transitions difficult.
Explicit 10+ years, Principal level, and mandatory cloud and platform expertise create highly rigid filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Design and build scalable, secure, cloud-native and hybrid data platforms and control-plane services supporting batch, streaming, API, and secure data-sharing use cases.
Architect large-scale distributed data and lakehouse platforms across multiple regions, clouds, on-premises, and sovereign environments, establishing federated governance and control-plane architectures.
Lead engineering practices including producing architecture decision records, reusable components, mentoring engineers, and collaborating with multiple stakeholders to drive platform and product goals.
Minimum Requirements
10+ years of hands-on data and software engineering experience with strong Java and/or Python, PySpark, API and backend service development skills.
Proven experience designing large-scale distributed or lakehouse platforms spanning regions, clouds, and execution environments.
Strong cloud data-platform experience with AWS and/or Azure services including S3, IAM, Glue, EKS, ADLS, Microsoft Entra ID, Azure RBAC, Key Vault, AKS, and relevant analytics services.
Bachelor’s degree in Computer Science, Engineering or related field, or equivalent hands-on experience.
Ideal Candidate Profile
Experienced in designing and implementing control-plane and data-plane architectures with a focus on governance, metadata, identity, access, and orchestration patterns.
Skilled in federated metadata, catalog, access, query, and secure data-sharing patterns considering latency, cost, regulatory, and operational factors.
Strong understanding of modern data architectures (lakehouse, medallion), batch and streaming data processing frameworks, cloud-native design, and production operational best practices.