Senior Manager Data Engineering

Amgen; Hyderabad; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Amgen is seeking a Senior Manager, Data Engineering (EDSE) to lead enterprise data initiatives, guiding high‑performing teams to deliver scalable data platforms and products. You will modernize the Enterprise Data Fabric (EDF) and enable advanced analytics, AI, and digital transformation across Finance, Supply Chain, R&D, and Operations.

With 12+ years in data engineering and 5+ years of leadership, you will drive data strategy, governance, and delivery across global teams, leveraging

Qualifications

• 12+ years of data engineering and analytics experience.
• 5+ years leading engineering teams and large programs.
• Strong hands-on with Databricks, Spark, PySpark, SQL, Python, AWS.
• Expertise in Data Fabric, Data Mesh, Lakehouse, and governance.
• Experience in Agile or SAFe delivery environments.
• Excellent communication and stakeholder management.

Responsibilities

• Lead and mentor global data engineering teams.
• Define the EDSE domain data roadmap and priorities.
• Oversee scalable data product pipelines from design to RunOps.
• Champion AI and automation to improve efficiency.
• Partner with architecture and platform teams to deliver governance.

Skills

Leadership

Strategic Planning

Data Engineering

Stakeholder Management

Cloud & Infra

AI/ ML Focus

Tools

Databricks

Spark

PySpark

SQL

Python

AWS

Cloud-native architectures

Job description

Career Category

Engineering

Job Description

Senior Manager - Data Engineering (EDSE)

About the Role

Lets do this. Lets change the world.

We are looking for an experienced Senior Manager, Data Engineering to lead strategic data engineering initiatives within Enterprise Data Strategy & Engineering (EDSE). This role will guide high-performing engineering teams, deliver enterprise-scale data platforms and data products, modernise the Enterprise Data Fabric (EDF), and enable advanced analytics, AI, and digital transformation across Finance, Supply Chain, Research & Development, Operations, and other business domains.

Key Responsibilities

Strategic Leadership

• Lead and develop data engineering teams responsible for enterprise data products, platforms, and mission-critical data solutions.
• Define and execute the domain data engineering roadmap in alignment with EDSE and enterprise priorities.
• Advance modern data engineering, cloud, AI, automation, and observability capabilities.
• Collaborate with business stakeholders, product teams, architecture, and platform engineering groups to deliver measurable business outcomes.

Delivery & Execution

• Oversee the design, development, deployment, and support of scalable data products and pipelines.
• Ensure strong delivery across build, enhancement, RunOps, and KTLO activities.
• Manage commitments, capacity, priorities, risks, and vendor execution.
• Set engineering standards, quality practices, and performance measures across the team.

Enterprise Data Platform & Architecture

• Lead implementation of the Enterprise Data Fabric (EDF), semantic layer, data products, and governance initiatives.
• Work with Enterprise Data Architecture and Platform Engineering teams to deliver scalable, secure, and reusable solutions.
• Promote metadata-driven engineering, automation, observability, data quality, and governance practices.

AI & Innovation

• Champion AI, traditional ML, Generative AI, Agentic AI, and automation to improve engineering efficiency and business value.
• Assess and adopt emerging technologies that accelerate delivery, improve data accessibility, and strengthen platform reliability.
• Drive innovation through reusable accelerators, engineering frameworks, and platform modernization.

People Leadership

• Build, mentor, and develop high-performing data engineering teams.
• Create a culture of technical excellence, collaboration, innovation, and continuous learning.
• Oversee performance management, career development, succession planning, and talent acquisition.
• Lead global, multi-vendor delivery teams aligned to organizational goals.

Required Qualifications

• 12+ years of experience in data engineering, data platforms, analytics engineering, or related fields.
• 5+ years of leadership experience managing engineering teams and large-scale delivery programs.
• Strong experience with Databricks, Spark, PySpark, SQL, Python, AWS, and cloud-native data architectures.
• Proven ability to build enterprise-scale data platforms, data products, and integration solutions.
• Strong understanding of Data Fabric, Data Mesh, Lakehouse, metadata management, and governance.
• Experience working in Agile or SAFe delivery environments.
• Excellent communication, stakeholder management, and leadership capabilities.

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