.**Description**:The primary responsibility of Senior Data Management Engineer is to build data pipelines, model and prepare data, perform complex data analysis to answer Business questions, build and automate data pipeline and quality framework to enable and promote self-service data pipelines, assist in operationalizing the AI / ML Engineering solutions. This role is expected to lead and guide other team members and evangelize the design patterns as well as coding standards. This role plays an active part in our Data Modernization project to migrate the from on premise platforms such as IBM Netezza to cloud project**Responsibilities**:- Team up with the engineering teams and enterprise architecture (EA) to define standards, designpatterns, accelerators, development practices, DevOps and CI/CD automation- Create and maintain the data ingestion, quality testing and audit framework- Conduct complex data analysis to answer the queries from Business Users or Technology teampartners either directly from Analysts or stemmed from one of the Reporting tools such as PowerBI, Tableau, OBIEE.- Build and automate the data ingestion, transformation and aggregation pipelines using AzureData Factory, Databricks / Spark, Snowflake, Kafka as well as Enterprise Scheduler tools such asCA Workload automation or Control M- Setup and evangelize the metadata driven approach to data pipelines to promote self service- Setup and continuously improve the data quality and audit monitoring as well as alerting- Constantly evaluate the process automation options and collaborate with engineering as well asarchitecture to review the proposed design.- Demonstrate mastery of build and release engineering principles and methodologies includingsource control, branch management, build and smoke testing, archiving and retention practices- Adhere to and enhance and document the design principles, best practices by collaborating withSolution and in some cases Enterprise Architects- Participate in and support the Data Academy and Data Literacy program to train the BusinessUsers and Technology teams on Data- Respond SLA driven production data quality or pipeline issues- Work in a fast-paced Agile/Scrum environment- Identify and assist with implementation of DevOps practices in support of fully automateddeployments- Document the Data Flow Diagrams, Data Models, Technical Data Mapping and ProductionSupport Information for Data Pipelines- Follow the Industry standard data security practices and evangelize the same across the team
