Principal Data Engineer – Data & Analytics (Global Supply Chain)
Amgen
Principal Data Engineer – Data & Analytics (Global Supply Chain)
Job Description
Role Description:
What you will do
- Design, develop, and maintain data solutions for data generation, collection, and processing
- Be a key team member that assists in design and development of the data pipeline
- Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems
- Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions
- Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks
- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs
- Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency
- Implement data security and privacy measures to protect sensitive data
- Leverage cloud platforms (AWS, Databricks preferred) to build scalable and efficient data solutions
- Collaborate and communicate effectively with product teams
- 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
- Explore new tools and technologies that will help to improve ETL platform performance
- Participate in sprint planning meetings and provide estimations on technical implementation
- Continuously monitor data governance activities and report on compliance, data quality issues, and the effectiveness of governance initiatives
What we expect of you
- 12 - 17 years of experience in Computer Science, IT or related field
Must-Have Skills
- Hands on experience with big data technologies and platforms, such as Databricks, Apache Spark (Databricks (PySpark, SparkSQL, Delta Lake) and AWS services (S3, EMR, Lambda, Glue, UC, Athena, Redshift, EKS), workflow orchestration, performance tuning on big data processing and the ability to work with large, complex datasets
- Hands-on experience in orchestrating large-scale data pipelines, performance tuning, lineage tracking, and observability frameworks
- Proficiency in data analysis tools (eg. SQL, Python) and experience with data visualization tools (Tableau, Power BI)
- Excellent problem-solving skills
- Experience with DevOps practices, version control (Git), CI/CD (Jenkins), and Infrastructure as Code
- Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model development
- Strong understanding of data modeling, data warehousing, and data integration concepts
- Working knowledge of unstructured data processing, vector stores, and AI-enablement for downstream analytics
- Strong understanding of SAP data models (ECC tables) and Supply Chain data domains
- Experience working in Agile/SAFe environments with distributed global teams
- Certified Data Engineer (preferred on Databricks or cloud environments)
- Machine Learning Certification (preferred)
- Excellent critical-thinking and problem-solving skills
- Strong communication and collaboration skills
- Demonstrated awareness of how to function in a team setting
- Demonstrated presentation skills
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
- Hands on experience with big data technologies and platforms, such as Databricks, Apache Spark (Databricks (PySpark, SparkSQL, Delta Lake) and AWS services (S3, EMR, Lambda, Glue, UC, Athena, Redshift, EKS), workflow orchestration, performance tuning on big data processing and the ability to work with large, complex datasetsintermediate
- Hands-on experience in orchestrating large-scale data pipelines, performance tuning, lineage tracking, and observability frameworksintermediate
- Proficiency in data analysis tools (eg. SQL, Python) and experience with data visualization tools (Tableau, Power BI)intermediate
- Excellent problem-solving skillsintermediate
- Experience with DevOps practices, version control (Git), CI/CD (Jenkins), and Infrastructure as Codeintermediate
- Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model developmentintermediate
- Strong understanding of data modeling, data warehousing, and data integration conceptsintermediate
- Working knowledge of unstructured data processing, vector stores, and AI-enablement for downstream analyticsintermediate
- Strong understanding of SAP data models (ECC tables) and Supply Chain data domainsintermediate
- Experience working in Agile/SAFe environments with distributed global teamsintermediate
- Certified Data Engineer (preferred on Databricks or cloud environments)intermediate
- Machine Learning Certification (preferred)intermediate
- Excellent critical-thinking and problem-solving skillsintermediate
- Strong communication and collaboration skillsintermediate
- Demonstrated awareness of how to function in a team settingintermediate
- Demonstrated presentation skillsintermediate
Required Qualifications
- 12 - 17 years of experience in Computer Science, IT or related field (experience)
Responsibilities
- Design, develop, and maintain data solutions for data generation, collection, and processing
- Be a key team member that assists in design and development of the data pipeline
- Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems
- Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions
- Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks
- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs
- Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency
- Implement data security and privacy measures to protect sensitive data
- Leverage cloud platforms (AWS, Databricks preferred) to build scalable and efficient data solutions
- Collaborate and communicate effectively with product teams
- 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
- Explore new tools and technologies that will help to improve ETL platform performance
- Participate in sprint planning meetings and provide estimations on technical implementation
- Continuously monitor data governance activities and report on compliance, data quality issues, and the effectiveness of governance initiatives
Tags & Categories
Principal Data Engineer – Data & Analytics (Global Supply Chain)
Amgen
Principal Data Engineer – Data & Analytics (Global Supply Chain)
Job Description
Role Description:
What you will do
- Design, develop, and maintain data solutions for data generation, collection, and processing
- Be a key team member that assists in design and development of the data pipeline
- Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems
- Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions
- Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks
- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs
- Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency
- Implement data security and privacy measures to protect sensitive data
- Leverage cloud platforms (AWS, Databricks preferred) to build scalable and efficient data solutions
- Collaborate and communicate effectively with product teams
- 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
- Explore new tools and technologies that will help to improve ETL platform performance
- Participate in sprint planning meetings and provide estimations on technical implementation
- Continuously monitor data governance activities and report on compliance, data quality issues, and the effectiveness of governance initiatives
What we expect of you
- 12 - 17 years of experience in Computer Science, IT or related field
Must-Have Skills
- Hands on experience with big data technologies and platforms, such as Databricks, Apache Spark (Databricks (PySpark, SparkSQL, Delta Lake) and AWS services (S3, EMR, Lambda, Glue, UC, Athena, Redshift, EKS), workflow orchestration, performance tuning on big data processing and the ability to work with large, complex datasets
- Hands-on experience in orchestrating large-scale data pipelines, performance tuning, lineage tracking, and observability frameworks
- Proficiency in data analysis tools (eg. SQL, Python) and experience with data visualization tools (Tableau, Power BI)
- Excellent problem-solving skills
- Experience with DevOps practices, version control (Git), CI/CD (Jenkins), and Infrastructure as Code
- Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model development
- Strong understanding of data modeling, data warehousing, and data integration concepts
- Working knowledge of unstructured data processing, vector stores, and AI-enablement for downstream analytics
- Strong understanding of SAP data models (ECC tables) and Supply Chain data domains
- Experience working in Agile/SAFe environments with distributed global teams
- Certified Data Engineer (preferred on Databricks or cloud environments)
- Machine Learning Certification (preferred)
- Excellent critical-thinking and problem-solving skills
- Strong communication and collaboration skills
- Demonstrated awareness of how to function in a team setting
- Demonstrated presentation skills
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
- Hands on experience with big data technologies and platforms, such as Databricks, Apache Spark (Databricks (PySpark, SparkSQL, Delta Lake) and AWS services (S3, EMR, Lambda, Glue, UC, Athena, Redshift, EKS), workflow orchestration, performance tuning on big data processing and the ability to work with large, complex datasetsintermediate
- Hands-on experience in orchestrating large-scale data pipelines, performance tuning, lineage tracking, and observability frameworksintermediate
- Proficiency in data analysis tools (eg. SQL, Python) and experience with data visualization tools (Tableau, Power BI)intermediate
- Excellent problem-solving skillsintermediate
- Experience with DevOps practices, version control (Git), CI/CD (Jenkins), and Infrastructure as Codeintermediate
- Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model developmentintermediate
- Strong understanding of data modeling, data warehousing, and data integration conceptsintermediate
- Working knowledge of unstructured data processing, vector stores, and AI-enablement for downstream analyticsintermediate
- Strong understanding of SAP data models (ECC tables) and Supply Chain data domainsintermediate
- Experience working in Agile/SAFe environments with distributed global teamsintermediate
- Certified Data Engineer (preferred on Databricks or cloud environments)intermediate
- Machine Learning Certification (preferred)intermediate
- Excellent critical-thinking and problem-solving skillsintermediate
- Strong communication and collaboration skillsintermediate
- Demonstrated awareness of how to function in a team settingintermediate
- Demonstrated presentation skillsintermediate
Required Qualifications
- 12 - 17 years of experience in Computer Science, IT or related field (experience)
Responsibilities
- Design, develop, and maintain data solutions for data generation, collection, and processing
- Be a key team member that assists in design and development of the data pipeline
- Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems
- Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions
- Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks
- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs
- Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency
- Implement data security and privacy measures to protect sensitive data
- Leverage cloud platforms (AWS, Databricks preferred) to build scalable and efficient data solutions
- Collaborate and communicate effectively with product teams
- 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
- Explore new tools and technologies that will help to improve ETL platform performance
- Participate in sprint planning meetings and provide estimations on technical implementation
- Continuously monitor data governance activities and report on compliance, data quality issues, and the effectiveness of governance initiatives
Tags & Categories