Data Solution Architect; Data Engineering

Position: Data Solution Architect (Data Engineering)
Location: Prakāshamnagar

About Amgen

Amgen harnesses the best of biology and technology to fight the world's toughest diseases, and make people's lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what's known today.

What You Will Do

Role

Description:

We are seeking a Data Solutions Architect to design, implement, and optimize scalable and high-performance data solutions that support enterprise analytics, AI-driven insights, and digital transformation initiatives. This role will focus on data strategy, architecture, governance, security, and operational efficiency, ensuring seamless data integration across modern cloud platforms. The ideal candidate will work closely with engineering teams, business stakeholders, and leadership to establish a future-ready data ecosystem, balancing performance, cost-efficiency, security, and usability.

This position requires expertise in modern cloud-based data architectures, data engineering best practices, and Scaled Agile methodologies.

Roles & Responsibilities :

Design and implement scalable, modular, and future-proof data architectures that support enterprise data lakes, data warehouses, and real-time analytics.
Develop enterprise-wide data frameworks that enable governed, secure, and accessible data across various business domains.
Define data modeling strategies to support structured and unstructured data, ensuring efficiency, consistency, and usability across analytical platforms.
Lead the development of high-performance data pipelines for batch and real-time data processing, integrating APIs, streaming sources, transactional systems, and external data platforms.
Optimize query performance, indexing, caching, and storage strategies to enhance scalability, cost efficiency, and analytical capabilities.
Establish data interoperability frameworks that enable seamless integration across multiple data sources and platforms.
Drive data governance strategies, ensuring security, compliance, access controls, and lineage tracking are embedded into enterprise data solutions.
Implement Data Ops best practices, including CI/CD for data pipelines, automated monitoring, and proactive issue resolution, to improve operational efficiency.
Lead Scaled Agile (SAFe) practices, facilitating Program Increment (PI) Planning, Sprint Planning, and Agile ceremonies, ensuring iterative delivery of enterprise data capabilities.
Collaborate with business stakeholders, product teams, and technology leaders to align data architecture strategies with organizational goals.
Act as a trusted advisor on emerging data technologies and trends, ensuring that the enterprise adopts cutting-edge data solutions that provide competitive advantage and long-term scalability.

What We Expect Of You

Must-Have

Skills:

Experience in data architecture, enterprise data management, and cloud-based analytics solutions.
Expertise in Databricks, cloud-native data platforms, and distributed computing frameworks.
Strong proficiency in modern data modeling techniques, including dimensional modeling, No

SQL, and data virtualization.
Experience designing high-performance ETL/ELT pipelines and real-time data processing solutions.
Deep understanding of data governance, security, metadata management, and access control frameworks.
Hands-on experience with CI/CD for data solutions, Data Ops automation, and infrastructure as code (IaaC).
Proven ability to collaborate with cross-functional teams, including business executives, data engineers, and analytics teams, to drive successful data initiatives.
Strong problem-solving, strategic thinking, and technical leadership skills.
Experienced with SQL/NOSQL database, vector database for large language models
Experienced with data modeling and performance tuning for both OLAP and OLTP databases
Experienced with Apache Spark
Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.),…