Senior Data Engineer Job at Amgen (Hyderabad)

Job Description

In this vital role as Specialist / Senior Data Engineer at Amgen you will be responsible for designing, building, maintaining, analyzing, and interpreting data to provide actionable insights that drive business decisions. This role involves working with large datasets, developing reports, supporting and executing data initiatives and, visualizing data to ensure data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture and ETL processes

Job Responsibility

Lead and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning for solving complex problems of R&D division
Oversee the development of data extraction, validation, and transformation techniques, ensuring ingested data is of high quality and compatible with downstream systems
Guide the team in writing and validating high-quality code for data ingestion, processing, and transformation, ensuring resiliency and fault tolerance
Drive the development of data tools and frameworks for managing and accessing data efficiently across the organization
Oversee the implementation of performance monitoring protocols across data pipelines, ensuring real-time visibility, alerts, and automated recovery mechanisms
Coach engineers in building dashboards and aggregations to monitor pipeline health and detect inefficiencies, ensuring optimal performance and cost-effectiveness
Lead the implementation of self-healing solutions, reducing failure points and improving pipeline stability and efficiency across multiple product features
Oversee data governance strategies, ensuring compliance with security policies, regulations, and data accessibility best practices
Guide engineers in data modeling, metadata management, and access control, ensuring structured data handling across various business use cases
Collaborate with business leaders, product owners, and cross-functional teams to ensure alignment of data architecture with product requirements and business objectives
Prepare team members for stakeholder discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios
Drive Agile and Scaled Agile (SAFe) methodologies, managing sprint backlogs, prioritization, and iterative improvements to enhance team velocity and project delivery
Stay up-to-date with emerging data technologies, industry trends, and best practices, ensuring the organization leverages the latest innovations in data engineering and architecture
Design, develop, and optimize data pipelines/workflows using Databricks (Spark, Delta Lake) for ingestion, transformation, and processing of large-scale data
Collaborate with Data Architects, Business SMEs, and Data Scientists to design and develop end-to-end data pipelines to meet fast paced business needs across geographic regions
Identify and resolve complex data-related challenges
Adhere to best practices for coding, testing, and designing reusable code/component
Analyze business and technical requirements and begin translating them into simple development tasks
Execute unit and integration tests, and contribute to maintaining software quality
Contribute to the maintenance and support of applications by monitoring performance and reporting issues
Use CI/CD pipelines as part of DevOps practices and assist in the release process

Requirements

Master's/Bachelor's degree and 8 to 13 years of Computer Science, IT or related field experience
Experience managing a team of data engineers
Experience architecting and building data and analytics solutions that extract, transform, and load data from multiple source systems
Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions
Proficiency in Python, PySpark, SQL
Experience with dimensional data modeling
Experience working with Apache Spark, Apache Airflow
Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and Dev Ops
Experienced with AWS or GCP or Azure cloud services
Understanding of end to end project/product life cycle
Well versed with full stack development & DataOps automation, logging frameworks, and pipeline orchestration tools
Strong analytical and problem-solving skills to address complex data challenges
Effective communication and interpersonal skills to collaborate with cross-functional teams
AWS Certified Data Engineer (must have)

Nice to have

Knowledge of Python/R, Databricks, cloud data platforms
Strong understanding of data governance frameworks, tools, and best practices
Knowledge of data protection regulations and compliance requirements (e.g., GDPR, CCPA)
Databricks Certificate (preferred)
Knowledge of Medallion Architecture will be an added advantage

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Senior Data Engineer

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