Senior Data Engineer
Amgen
Senior Data Engineer
Job Description
ABOUT AMGEN
What you will do
- Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric.
- Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture.
- Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency.
- Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance.
- Ensure data security, compliance, and role-based access control (RBAC) across data environments.
- Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets.
- Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring.
- Implement data virtualization techniques to provide seamless access to data across multiple storage systems.
- Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals.
- Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures.
What we expect of you
- 9 to 12 years of Computer Science, IT or related field experience
- AWS Certified Data Engineer preferred
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
Must-Have Skills
- Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
- Proficiency in workflow orchestration, performance tuning on big data processing.
- Strong understanding of AWS services
- Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures.
- Ability to quickly learn, adapt and apply new technologies
- Strong problem-solving and analytical skills
- Excellent communication and teamwork skills
- Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices.
- Experience in writing APIs to make the data available to the consumers
- 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 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
Good-to-Have Skills
- Good to have deep expertise in Biotech & Pharma industries
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
30,000 - 50,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.intermediate
- Proficiency in workflow orchestration, performance tuning on big data processing.intermediate
- Strong understanding of AWS servicesintermediate
- Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures.intermediate
- Ability to quickly learn, adapt and apply new technologiesintermediate
- Strong problem-solving and analytical skillsintermediate
- Excellent communication and teamwork skillsintermediate
- Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices.intermediate
- Good to have deep expertise in Biotech & Pharma industriesintermediate
- Experience in writing APIs to make the data available to the consumersintermediate
- Experienced with SQL/NOSQL database, vector database for large language modelsintermediate
- Experienced with data modeling and performance tuning for both OLAP and OLTP databasesintermediate
- 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 Opsintermediate
Required Qualifications
- 9 to 12 years of Computer Science, IT or related field experience (experience)
- AWS Certified Data Engineer preferred (experience)
- Databricks Certificate preferred (experience)
- Scaled Agile SAFe certification preferred (experience)
Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric.
- Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture.
- Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency.
- Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance.
- Ensure data security, compliance, and role-based access control (RBAC) across data environments.
- Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets.
- Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring.
- Implement data virtualization techniques to provide seamless access to data across multiple storage systems.
- Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals.
- Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures.
Tags & Categories
Senior Data Engineer
Amgen
Senior Data Engineer
Job Description
ABOUT AMGEN
What you will do
- Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric.
- Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture.
- Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency.
- Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance.
- Ensure data security, compliance, and role-based access control (RBAC) across data environments.
- Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets.
- Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring.
- Implement data virtualization techniques to provide seamless access to data across multiple storage systems.
- Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals.
- Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures.
What we expect of you
- 9 to 12 years of Computer Science, IT or related field experience
- AWS Certified Data Engineer preferred
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
Must-Have Skills
- Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
- Proficiency in workflow orchestration, performance tuning on big data processing.
- Strong understanding of AWS services
- Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures.
- Ability to quickly learn, adapt and apply new technologies
- Strong problem-solving and analytical skills
- Excellent communication and teamwork skills
- Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices.
- Experience in writing APIs to make the data available to the consumers
- 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 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
Good-to-Have Skills
- Good to have deep expertise in Biotech & Pharma industries
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
30,000 - 50,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.intermediate
- Proficiency in workflow orchestration, performance tuning on big data processing.intermediate
- Strong understanding of AWS servicesintermediate
- Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures.intermediate
- Ability to quickly learn, adapt and apply new technologiesintermediate
- Strong problem-solving and analytical skillsintermediate
- Excellent communication and teamwork skillsintermediate
- Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices.intermediate
- Good to have deep expertise in Biotech & Pharma industriesintermediate
- Experience in writing APIs to make the data available to the consumersintermediate
- Experienced with SQL/NOSQL database, vector database for large language modelsintermediate
- Experienced with data modeling and performance tuning for both OLAP and OLTP databasesintermediate
- 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 Opsintermediate
Required Qualifications
- 9 to 12 years of Computer Science, IT or related field experience (experience)
- AWS Certified Data Engineer preferred (experience)
- Databricks Certificate preferred (experience)
- Scaled Agile SAFe certification preferred (experience)
Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric.
- Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture.
- Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency.
- Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance.
- Ensure data security, compliance, and role-based access control (RBAC) across data environments.
- Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets.
- Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring.
- Implement data virtualization techniques to provide seamless access to data across multiple storage systems.
- Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals.
- Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures.
Tags & Categories