Merck
Application Enablement - Senior Data Engineer (Hybrid)
Merck, Boston, Massachusetts, us, 02298
Qualifications
B.Sc. or higher degree in computer sciences or an equivalent field required3-6 years of relevant experienceSolid experience with AWS services (S3, IAM, Redshift, Sagemaker, Glue, Lambda, Step Functions, CloudWatch)Experience with platforms like Databricks, DataikuProficient in Python / JavaProficient in Jenkins, CloudFormation, Terraform, Git, Docker2-3 years of Spark - PySparkAbility to work in cross functional teamsResponsibilities
Data Engineer for Application Enablement will be part of the Scientific data consumption product teamIn this role, they will work with product teams, technical teams, SMEs and stakeholders to define, design, develop and maintain data productsIn this role, they will work closely with multi-disciplinary teams helping to solve complex data problems and building data products to enable AI/MLThey will have to understand complexities in Research data and work with product team to make data understandable and analytics readyThe Scientific Data Consumption Product Line (SDC) provides a combination of technology, infrastructure, software, and personnel to support large scale scientific data consumption and computing comprising of the Scientific Data Platform, Modeling and Machine Learning Platforms, High Performance Computing (HPC) workflows, and "experience" products such as visualization, modeling workbenches, and analytics development environmentsDesign, develop and maintain data pipelines to extract data from a variety of sources and populate data lake and data warehouseDevelop the various data transformation rules and data modeling capabilitiesCollaborate with Product Analyst, Data Scientists, and Machine Learning Engineers to identify and transform data to make data understandableWork with data governance team and implement data quality checks and maintain data catalogsUse Orchestration, logging, and monitoring tools to build resilient pipelinesUse test driven development methodology when building ELT/ETL pipelinesUse Git for version control and understand various branching strategiesWork as part of an agile teamCreate technical documentation as neededBenefits
Final determinations with respect to salary will take into account a number of factors, which may include, but not be limited to the primary work location and the chosen candidate's relevant skills, experience, and educationAvailable benefits include bonus eligibility, health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and sick days
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B.Sc. or higher degree in computer sciences or an equivalent field required3-6 years of relevant experienceSolid experience with AWS services (S3, IAM, Redshift, Sagemaker, Glue, Lambda, Step Functions, CloudWatch)Experience with platforms like Databricks, DataikuProficient in Python / JavaProficient in Jenkins, CloudFormation, Terraform, Git, Docker2-3 years of Spark - PySparkAbility to work in cross functional teamsResponsibilities
Data Engineer for Application Enablement will be part of the Scientific data consumption product teamIn this role, they will work with product teams, technical teams, SMEs and stakeholders to define, design, develop and maintain data productsIn this role, they will work closely with multi-disciplinary teams helping to solve complex data problems and building data products to enable AI/MLThey will have to understand complexities in Research data and work with product team to make data understandable and analytics readyThe Scientific Data Consumption Product Line (SDC) provides a combination of technology, infrastructure, software, and personnel to support large scale scientific data consumption and computing comprising of the Scientific Data Platform, Modeling and Machine Learning Platforms, High Performance Computing (HPC) workflows, and "experience" products such as visualization, modeling workbenches, and analytics development environmentsDesign, develop and maintain data pipelines to extract data from a variety of sources and populate data lake and data warehouseDevelop the various data transformation rules and data modeling capabilitiesCollaborate with Product Analyst, Data Scientists, and Machine Learning Engineers to identify and transform data to make data understandableWork with data governance team and implement data quality checks and maintain data catalogsUse Orchestration, logging, and monitoring tools to build resilient pipelinesUse test driven development methodology when building ELT/ETL pipelinesUse Git for version control and understand various branching strategiesWork as part of an agile teamCreate technical documentation as neededBenefits
Final determinations with respect to salary will take into account a number of factors, which may include, but not be limited to the primary work location and the chosen candidate's relevant skills, experience, and educationAvailable benefits include bonus eligibility, health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and sick days
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