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Strong employer brand but highly senior, niche Databricks/AWS and AI-enabled platform expertise reduces applicant density.
Role requires deep data-platform, governance, Databricks and mastering experience, making cross-industry fit limited and specialized.
Explicit seniority plus specialized Databricks, AWS, governance and AI-tooling requirements create a high filter for shortlisting.
Lead the transition and delivery of enterprise data pipelines to a cloud-native data platform leveraging AWS and Databricks technologies.
Drive AI-assisted engineering adoption for data pipeline development, including context engineering and reusable AI playbooks to improve efficiency and code quality.
Provide technical direction for offshore teams, establish platform standards, and ensure integration with data mastering systems and semantic modeling for governed data usage.
10+ years of experience in data engineering, cloud data architecture, or related domains with senior technical leadership roles.
Hands-on expertise with Databricks, AWS data services (S3, Glue, Lambda, Lake Formation, Kinesis), and cloud-native data platform architectures.
Practical experience with AI-assisted engineering tools such as Claude, GitHub CoPilot, or LLMs for code generation and optimization.
Location requirement: Hyderabad; Shift: 12 to 9 pm IST; Hybrid work model with 9 days per month in-office work.
Experienced senior individual contributor comfortable bridging hands-on engineering with architectural strategy and delivery leadership.
Deep knowledge of cloud-native governed data lakehouse architectures, semantic data modeling, and data mastering integrations relevant to financial datasets.
Proven ability to influence technical direction across global teams without direct reporting authority and promote engineering best practices including AI-assisted workflows.