Data Engineering Manager
Amgen
Data Engineering Manager
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- 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 running 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 key partner discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios.
- Drive Agile and Scaled Agile (SAFe) methodologies, handling 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 uses the latest innovations in data engineering and architecture.
What we expect of you
- Doctorate degree OR Master’s degree and 4 to 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree and 6 to 8 years of experience in Computer Science, IT or related field OR Diploma and 10 to 12 years of experience in Computer Science, IT or related field
- Experience leading a team of data engineers in the R&D domain of biotech/pharma companies.
- Experience architecting and building data and analytics solutions that extract, transform, and load data from multiple source systems.
- Data Engineering experience in R&D for Biotechnology or pharma industry
- 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 preferred
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
- Project Management certifications preferred
- Data Engineering Management experience in Biotech/Pharma is a plus
- Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc.
Must-Have Skills
- Excellent analytical and troubleshooting skills
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation
- Ability to handle multiple priorities successfully
- Team-oriented, with a focus on achieving team goals
- Strong presentation and public speaking skills
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
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
80,000 - 120,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Excellent analytical and troubleshooting skillsintermediate
- Strong verbal and written communication skillsintermediate
- Ability to work effectively with global, virtual teamsintermediate
- High degree of initiative and self-motivationintermediate
- Ability to handle multiple priorities successfullyintermediate
- Team-oriented, with a focus on achieving team goalsintermediate
- Strong presentation and public speaking skillsintermediate
Required Qualifications
- Doctorate degree OR Master’s degree and 4 to 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree and 6 to 8 years of experience in Computer Science, IT or related field OR Diploma and 10 to 12 years of experience in Computer Science, IT or related field (experience)
- Experience leading a team of data engineers in the R&D domain of biotech/pharma companies. (experience)
- Experience architecting and building data and analytics solutions that extract, transform, and load data from multiple source systems. (experience)
- Data Engineering experience in R&D for Biotechnology or pharma industry (experience)
- Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions. (experience)
- Proficiency in Python, PySpark, SQL. (experience)
- Experience with dimensional data modeling. (experience)
- Experience working with Apache Spark, Apache Airflow. (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 Dev Ops. (experience)
- Experienced with AWS or GCP or Azure cloud services. (experience)
- Understanding of end to end project/product life cycle (experience)
- Well versed with full stack development & DataOps automation, logging frameworks, and pipeline orchestration tools. (experience)
- Strong analytical and problem-solving skills to address complex data challenges. (experience)
- Effective communication and interpersonal skills to collaborate with cross-functional teams. (experience)
- AWS Certified Data Engineer preferred (experience)
- Databricks Certificate preferred (experience)
- Scaled Agile SAFe certification preferred (experience)
- Project Management certifications preferred (experience)
- Data Engineering Management experience in Biotech/Pharma is a plus (experience)
- Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc. (experience)
Responsibilities
- 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 running 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 key partner discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios.
- Drive Agile and Scaled Agile (SAFe) methodologies, handling 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 uses the latest innovations in data engineering and architecture.
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 Engineering Manager
Amgen
Data Engineering Manager
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- 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 running 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 key partner discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios.
- Drive Agile and Scaled Agile (SAFe) methodologies, handling 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 uses the latest innovations in data engineering and architecture.
What we expect of you
- Doctorate degree OR Master’s degree and 4 to 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree and 6 to 8 years of experience in Computer Science, IT or related field OR Diploma and 10 to 12 years of experience in Computer Science, IT or related field
- Experience leading a team of data engineers in the R&D domain of biotech/pharma companies.
- Experience architecting and building data and analytics solutions that extract, transform, and load data from multiple source systems.
- Data Engineering experience in R&D for Biotechnology or pharma industry
- 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 preferred
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
- Project Management certifications preferred
- Data Engineering Management experience in Biotech/Pharma is a plus
- Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc.
Must-Have Skills
- Excellent analytical and troubleshooting skills
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation
- Ability to handle multiple priorities successfully
- Team-oriented, with a focus on achieving team goals
- Strong presentation and public speaking skills
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
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
80,000 - 120,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Excellent analytical and troubleshooting skillsintermediate
- Strong verbal and written communication skillsintermediate
- Ability to work effectively with global, virtual teamsintermediate
- High degree of initiative and self-motivationintermediate
- Ability to handle multiple priorities successfullyintermediate
- Team-oriented, with a focus on achieving team goalsintermediate
- Strong presentation and public speaking skillsintermediate
Required Qualifications
- Doctorate degree OR Master’s degree and 4 to 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree and 6 to 8 years of experience in Computer Science, IT or related field OR Diploma and 10 to 12 years of experience in Computer Science, IT or related field (experience)
- Experience leading a team of data engineers in the R&D domain of biotech/pharma companies. (experience)
- Experience architecting and building data and analytics solutions that extract, transform, and load data from multiple source systems. (experience)
- Data Engineering experience in R&D for Biotechnology or pharma industry (experience)
- Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions. (experience)
- Proficiency in Python, PySpark, SQL. (experience)
- Experience with dimensional data modeling. (experience)
- Experience working with Apache Spark, Apache Airflow. (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 Dev Ops. (experience)
- Experienced with AWS or GCP or Azure cloud services. (experience)
- Understanding of end to end project/product life cycle (experience)
- Well versed with full stack development & DataOps automation, logging frameworks, and pipeline orchestration tools. (experience)
- Strong analytical and problem-solving skills to address complex data challenges. (experience)
- Effective communication and interpersonal skills to collaborate with cross-functional teams. (experience)
- AWS Certified Data Engineer preferred (experience)
- Databricks Certificate preferred (experience)
- Scaled Agile SAFe certification preferred (experience)
- Project Management certifications preferred (experience)
- Data Engineering Management experience in Biotech/Pharma is a plus (experience)
- Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc. (experience)
Responsibilities
- 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 running 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 key partner discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios.
- Drive Agile and Scaled Agile (SAFe) methodologies, handling 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 uses the latest innovations in data engineering and architecture.
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