Data Engineer Data Ops And Automation Us Fortune 500 Multinational Automotive Firm Years
Responsibilities:
1)Maintain, support, and enhance the business intelligence data backend, including data lakes, Kafka integrations, Jira and Jenkins pipelines that this team works on.
-Performs needed assessments
-Implement frameworks and structures for data warehouse and lake/repository.
-Manage cloud and/or on-premises solutions for data transfer and storage.
-Establish data structures for all enterprise data stored in business intelligence systems.
-Go to person for AWS AMI management, pipeline stability and upkeeping of it.
-Participate in new tools evaluation, implementation of it within the Data Lake environment or in the ecosystem.
-Oversee and ensure that new systems implemented at the enterprise level follow data quality guidelines.
2)Keeps abreast of new business intelligence technologies; makes periodic recommendations for overall improvements.
3)Partner with Dev Ops organization
4)Terraform
-For constant monitoring of the cloud infrastructure of D and A team.
-Implement best practices, optimization changes suggested.
-Support the CI/CD pipelines that are leveraged by D and A team.
-Build deployment automations and integrate with Jira for smooth releases.
-Personally responsible for release management within the D and A organization that happen in agile methodology.
5)Maintain, support, and enhance the business intelligence data backend, including data warehouses and data lakes.
-Performs needed assessments
-Implement data transformations and data structures for data warehouse and lake/repository.
-Manage cloud and/or on-premises solutions for data transfer and storage.
-Establish data structures for all enterprise data stored in business intelligence systems.
6)Collaborate and work with data analysts in various functions to ensure that data meets their reporting and analysis needs.
-Establish interfaces between the data warehouse and reporting tools, such as PowerBI.
-Assist data analysts with connecting reporting and analytics software to data warehouses, lakes, and other data sources.
-Manage access and permissions to data.
7)Provide technical guidance for design and implementation of data governance systems and policy.
-Work with the data governance team to manage an enterprise wide data governance framework, with a focus on improvement of data quality and the protection of sensitive data through modifications to organization behavior policies and standards, principles, governance metrics, processes, related tools, and data architecture.
-Monitor data quality, identify data quality issues, oversee remediation plans, implementation of data controls, and manage data quality remediation strategies. Define data quality strategy and participate in a data quality working group.
-Oversee and ensure that new systems implemented at the enterprise level follow data quality guidelines.
8)Keeps abreast of new business intelligence technologies; makes periodic recommendations for overall improvements.
Qualifications:
Bachelor’s Degree in STEM disciplines plus at least 5-7 years of experience managing data warehouse and/or business intelligence systems. An advanced degree or certifications in a related field is a plus.
Knowledge, Skills and Abilities:
-Demonstrated experience with setting up data structures (tables and views) for use with modern analytics software/tools.
-Expertise with Snowflake Data Warehouse, Amazon Web Services, SQL-based database systems, and/or other enterprise data warehouse solutions.
-experience working with integration tools such as APIs, Web Services, JDBC/ODBC connectors, and other integration technologies.
-experience working with programming languages used in ETL and/or ELT environments, such as SQL and Python.
-Extensive experience in terraform, ansible, Jira, Jenkins and prior working knowledge of automation framework in a cloud infrastructure (public preferably) will be a big plus.