Data Engineer
Amgen
Data Engineer
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- Design, develop, and maintain complex ETL/ELT data pipelines in Databricks using PySpark, Scala, and SQL to process large-scale datasets
- Understand the biotech/pharma or related domains & build highly efficient data pipelines to migrate and deploy complex data across systems
- Design and Implement solutions to enable unified data access, governance, and interoperability across hybrid cloud environments
- Ingest and transform structured and unstructured data from databases (PostgreSQL, MySQL, SQL Server, MongoDB etc.), APIs, logs, event streams, images, pdf, and third-party platforms
- Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring
- Expert in data quality, data validation and verification frameworks
- Innovate, explore and implement new tools and technologies to enhance efficient data processing
- Proactively identify and implement opportunities to automate tasks and develop reusable frameworks
- Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value
- Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories
- Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle
- Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions
What we expect of you
- Master’s degree and 1 to 3 years of Computer Science, IT or related field experience OR
- Bachelor’s degree and 3 to 5 years of Computer Science, IT or related field experience OR
- Diploma and 7 to 9 years of Computer Science, IT or related field experience
- 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
- 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
- AWS Certified Data Engineer preferred
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
- Data Engineering experience in Biotechnology or pharma industry
- 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 DevOps
Must-Have Skills
- PySpark
- Scala
- SQL
- Databricks
- Apache Spark
- AWS
- Python
- Scaled Agile methodologies
- Workflow orchestration
- Performance tuning on big data processing
- AWS services
- JIRA
- Confluence
- Agile DevOps tools
- Data quality, data validation and verification frameworks
- ETL/ELT data pipelines
- Data modeling
- Data governance
- PostgreSQL
- MySQL
- SQL Server
- MongoDB
- APIs
- Git
- Subversion
- CI/CD (Jenkins, Maven)
- Automated unit testing
- DevOps
- SQL/NOSQL database
- Vector database for large language models
- OLAP and OLTP databases
What you can expect of us
- Competitive benefits
- Collaborative culture
- Support for professional and personal growth and well-being
- Competitive and comprehensive Total Rewards Plans that are aligned with local industry standards
Locations
- Hyderabad, India (Remote)
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
15,000 - 30,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- PySparkintermediate
- Scalaintermediate
- SQLintermediate
- Databricksintermediate
- Apache Sparkintermediate
- AWSintermediate
- Pythonintermediate
- Scaled Agile methodologiesintermediate
- Workflow orchestrationintermediate
- Performance tuning on big data processingintermediate
- AWS servicesintermediate
- JIRAintermediate
- Confluenceintermediate
- Agile DevOps toolsintermediate
- Data quality, data validation and verification frameworksintermediate
- ETL/ELT data pipelinesintermediate
- Data modelingintermediate
- Data governanceintermediate
- PostgreSQLintermediate
- MySQLintermediate
- SQL Serverintermediate
- MongoDBintermediate
- APIsintermediate
- Gitintermediate
- Subversionintermediate
- CI/CD (Jenkins, Maven)intermediate
- Automated unit testingintermediate
- DevOpsintermediate
- SQL/NOSQL databaseintermediate
- Vector database for large language modelsintermediate
- OLAP and OLTP databasesintermediate
Required Qualifications
- Master’s degree and 1 to 3 years of Computer Science, IT or related field experience OR (experience)
- Bachelor’s degree and 3 to 5 years of Computer Science, IT or related field experience OR (experience)
- Diploma and 7 to 9 years of Computer Science, IT or related field experience (experience)
- Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies (experience)
- Proficiency in workflow orchestration, performance tuning on big data processing (experience)
- Strong understanding of AWS services (experience)
- Ability to quickly learn, adapt and apply new technologies (experience)
- Strong problem-solving and analytical skills (experience)
- Excellent communication and teamwork skills (experience)
- Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices (experience)
- AWS Certified Data Engineer preferred (experience)
- Databricks Certificate preferred (experience)
- Scaled Agile SAFe certification preferred (experience)
- Data Engineering experience in Biotechnology or pharma industry (experience)
- Experience in writing APIs to make the data available to the consumers (experience)
- Experienced with SQL/NOSQL database, vector database for large language models (experience)
- Experienced with data modeling and performance tuning for both OLAP and OLTP databases (experience)
- 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 DevOps (experience)
Responsibilities
- Design, develop, and maintain complex ETL/ELT data pipelines in Databricks using PySpark, Scala, and SQL to process large-scale datasets
- Understand the biotech/pharma or related domains & build highly efficient data pipelines to migrate and deploy complex data across systems
- Design and Implement solutions to enable unified data access, governance, and interoperability across hybrid cloud environments
- Ingest and transform structured and unstructured data from databases (PostgreSQL, MySQL, SQL Server, MongoDB etc.), APIs, logs, event streams, images, pdf, and third-party platforms
- Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring
- Expert in data quality, data validation and verification frameworks
- Innovate, explore and implement new tools and technologies to enhance efficient data processing
- Proactively identify and implement opportunities to automate tasks and develop reusable frameworks
- Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value
- Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories
- Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle
- Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions
Benefits
- general: Competitive benefits
- general: Collaborative culture
- general: Support for professional and personal growth and well-being
- general: Competitive and comprehensive Total Rewards Plans that are aligned with local industry standards
Tags & Categories
Data Engineer
Amgen
Data Engineer
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- Design, develop, and maintain complex ETL/ELT data pipelines in Databricks using PySpark, Scala, and SQL to process large-scale datasets
- Understand the biotech/pharma or related domains & build highly efficient data pipelines to migrate and deploy complex data across systems
- Design and Implement solutions to enable unified data access, governance, and interoperability across hybrid cloud environments
- Ingest and transform structured and unstructured data from databases (PostgreSQL, MySQL, SQL Server, MongoDB etc.), APIs, logs, event streams, images, pdf, and third-party platforms
- Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring
- Expert in data quality, data validation and verification frameworks
- Innovate, explore and implement new tools and technologies to enhance efficient data processing
- Proactively identify and implement opportunities to automate tasks and develop reusable frameworks
- Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value
- Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories
- Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle
- Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions
What we expect of you
- Master’s degree and 1 to 3 years of Computer Science, IT or related field experience OR
- Bachelor’s degree and 3 to 5 years of Computer Science, IT or related field experience OR
- Diploma and 7 to 9 years of Computer Science, IT or related field experience
- 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
- 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
- AWS Certified Data Engineer preferred
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
- Data Engineering experience in Biotechnology or pharma industry
- 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 DevOps
Must-Have Skills
- PySpark
- Scala
- SQL
- Databricks
- Apache Spark
- AWS
- Python
- Scaled Agile methodologies
- Workflow orchestration
- Performance tuning on big data processing
- AWS services
- JIRA
- Confluence
- Agile DevOps tools
- Data quality, data validation and verification frameworks
- ETL/ELT data pipelines
- Data modeling
- Data governance
- PostgreSQL
- MySQL
- SQL Server
- MongoDB
- APIs
- Git
- Subversion
- CI/CD (Jenkins, Maven)
- Automated unit testing
- DevOps
- SQL/NOSQL database
- Vector database for large language models
- OLAP and OLTP databases
What you can expect of us
- Competitive benefits
- Collaborative culture
- Support for professional and personal growth and well-being
- Competitive and comprehensive Total Rewards Plans that are aligned with local industry standards
Locations
- Hyderabad, India (Remote)
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
15,000 - 30,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- PySparkintermediate
- Scalaintermediate
- SQLintermediate
- Databricksintermediate
- Apache Sparkintermediate
- AWSintermediate
- Pythonintermediate
- Scaled Agile methodologiesintermediate
- Workflow orchestrationintermediate
- Performance tuning on big data processingintermediate
- AWS servicesintermediate
- JIRAintermediate
- Confluenceintermediate
- Agile DevOps toolsintermediate
- Data quality, data validation and verification frameworksintermediate
- ETL/ELT data pipelinesintermediate
- Data modelingintermediate
- Data governanceintermediate
- PostgreSQLintermediate
- MySQLintermediate
- SQL Serverintermediate
- MongoDBintermediate
- APIsintermediate
- Gitintermediate
- Subversionintermediate
- CI/CD (Jenkins, Maven)intermediate
- Automated unit testingintermediate
- DevOpsintermediate
- SQL/NOSQL databaseintermediate
- Vector database for large language modelsintermediate
- OLAP and OLTP databasesintermediate
Required Qualifications
- Master’s degree and 1 to 3 years of Computer Science, IT or related field experience OR (experience)
- Bachelor’s degree and 3 to 5 years of Computer Science, IT or related field experience OR (experience)
- Diploma and 7 to 9 years of Computer Science, IT or related field experience (experience)
- Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies (experience)
- Proficiency in workflow orchestration, performance tuning on big data processing (experience)
- Strong understanding of AWS services (experience)
- Ability to quickly learn, adapt and apply new technologies (experience)
- Strong problem-solving and analytical skills (experience)
- Excellent communication and teamwork skills (experience)
- Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices (experience)
- AWS Certified Data Engineer preferred (experience)
- Databricks Certificate preferred (experience)
- Scaled Agile SAFe certification preferred (experience)
- Data Engineering experience in Biotechnology or pharma industry (experience)
- Experience in writing APIs to make the data available to the consumers (experience)
- Experienced with SQL/NOSQL database, vector database for large language models (experience)
- Experienced with data modeling and performance tuning for both OLAP and OLTP databases (experience)
- 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 DevOps (experience)
Responsibilities
- Design, develop, and maintain complex ETL/ELT data pipelines in Databricks using PySpark, Scala, and SQL to process large-scale datasets
- Understand the biotech/pharma or related domains & build highly efficient data pipelines to migrate and deploy complex data across systems
- Design and Implement solutions to enable unified data access, governance, and interoperability across hybrid cloud environments
- Ingest and transform structured and unstructured data from databases (PostgreSQL, MySQL, SQL Server, MongoDB etc.), APIs, logs, event streams, images, pdf, and third-party platforms
- Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring
- Expert in data quality, data validation and verification frameworks
- Innovate, explore and implement new tools and technologies to enhance efficient data processing
- Proactively identify and implement opportunities to automate tasks and develop reusable frameworks
- Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value
- Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories
- Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle
- Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions
Benefits
- general: Competitive benefits
- general: Collaborative culture
- general: Support for professional and personal growth and well-being
- general: Competitive and comprehensive Total Rewards Plans that are aligned with local industry standards
Tags & Categories