Sr Data Platform Lead Job at Amgen (Hyderabad)

Job Description

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Job Responsibility

Act as a platform lead for delivery of data platform capabilities that enable next-gen data platform architecture, with a strong focus on Databricks platform and DQ platform features and services
Evaluate and enable Databricks platform capabilities through technical assessments and proof‑of‑concepts (PoCs), ensuring alignment with next-gen data platform architectural patterns and enterprise standards
Design, build, and productionize reusable platform frameworks, accelerators, and reference implementations that can be leveraged by next-gen data platform delivery teams (excluding ownership of data pipeline architecture or implementation)
Enable data governance, metadata layer, and data bundle capabilities by designing and implementing platform‑level integrations between Databricks and Collibra, Amgen’s enterprise data governance platform
Build platform‑level tooling and automation to support proactive governance, cost optimization, and best‑practice enforcement across Databricks and related data platform services
Define and enable platform observability capabilities, including KPIs, metrics, and telemetry for monitoring performance, usage, reliability, and cost of Databricks services
Identify and implement governed self‑service platform capabilities for data engineers through self-service portal, using Python‑based microservices deployed on Docker and Kubernetes
Lead user enablement and adoption initiatives, including onboarding content, guided learning experiences, workshops, and best‑practice sharing for the Databricks user community
Drive engineering excellence and adoption of AI across platform capabilities and solutions built, promoting modern engineering practices, automation, and responsible use of AI‑driven features
Enable key business programs and strategic initiatives by translating initiative‑driven requirements into scalable, reusable data platform capabilities, in alignment with next-gen data platform principles
Collaborate closely with Enterprise Data Architecture (EDA), governance, platform operations, and delivery teams to ensure platform capabilities are aligned, consumable, and enterprise‑ready

Requirements

Master's degree OR Bachelor's degree in computer science or engineering field and 8 to 13 years of relevant experience
Strong hands‑on experience with various capabilities of Databricks, from Compute to Storage and from Unity Catalog to Data Engineering to BI and AI/ML capabilities, with a focus on governance and enterprise enablement
Proven hands‑on experience with cloud platforms, with strong preference for AWS (experience with Azure or GCP also acceptable)
Experience leading Data Quality platform initiatives (e.g., Ataccama, Monte Carlo), including tool evaluation, implementation, enterprise-wide adoption, and integration with enterprise DQ solutions
Experience owning and managing Databricks platform environments, including workspace architecture, environment strategy (dev/test/prod), and lifecycle management at scale
Proven ability to establish and enforce platform standards and operating models, including cluster policies, cost management, and workload orchestration frameworks
Strong focus on platform enablement and developer experience, including building reusable frameworks, defining best practices, and supporting engineering teams in adopting the platform effectively
Exposure to AI/ML capabilities on Databricks, including enabling AI‑driven features or accelerating adoption of AI‑assisted engineering practices
Solid knowledge of SQL and relational / dimensional data modelling, sufficient to support platform integrations, governance, and observability use cases
Experience working with core AWS services such as EKS, EC2, S3, Lambda, Glue, EMR, RDS, and Redshift/Spectrum, particularly in platform or shared‑services contexts
Strong analytical and problem‑solving skills, with the ability to design scalable, reusable solutions for complex data platform challenges
Experience working in Agile delivery environments, with exposure to tools such as Jira or Jira Align

Nice to have

Experience contributing to or enabling self‑service platforms or portals (front‑end or API‑driven), including collaboration with front‑end teams (e.g., React‑based portals)
Proficiency in Python‑based microservices development, including designing and deploying APIs and services that enable platform capabilities
Experience building platform APIs and services with Databricks SDKs and REST APIs for provisioning, managing, or governing Databricks environments and managing workspaces, clusters, jobs, users, permissions, and related platform features
Experience with DQplatforms like Ataccama or Monte Carlo used in association with Data Engineering platforms
Familiarity with platform observability, telemetry, or cost‑optimization patterns for large‑scale data platforms
Experience enabling data governance, metadata, or lineage integrations between data platforms and enterprise governance tools (e.g., Collibra)
Experience working with SQL/NoSQL databases and vector databases, particularly in the context of LLM‑enabled or AI‑assisted platform solutions
Experience with CI/CD pipelines, containerization (Docker), and Kubernetes/EKS, and applying these practices to enterprise platform services
Strong understanding of software engineering best practices, including version control, automated testing, continuous integration, and production‑grade design
Certifications (preferred but not required): AWS Certified Data Engineer, Databricks Certification, SAFe Agile Certification

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