Senior Manager IT Data Engineering at Smithfield Foods in Smithfield, VA
Core Responsibilities Assist in leading data platform design and deployment including the following components:
data ingestion, data storage, compute, pipeline orchestration, source code control, error/event handling, CI/CD, access control. Partner with data architects and analysts to develop reusable data pipeline design patterns. Work with IT, business and data management teams to understand data needs and develop solutions. Plan, execute and manage the integration of data across the enterprise's data assets. Develop standards, designs, data maps; developing and modifying functions, programs, routines and stored procedures to export, transform and load data. Collaborate with IT and Business Analytics teams to design and model application data structures, data store and integration processes across a global application landscape. Participate in the selection of emerging technologies for data management, storage, and analytics. Develop and executing test plans and scenarios including data design, tool design, data extract/transform, networks and hardware. Support business case creation. Promote architectural standards and consistency. Assist in assessing solution option risks and develop mitigation plans. Work with business partners to understand requirements for data use, analysis, and quality. Identify and aggregate data from internal and external sources. Coordinate with data owners to ensure data accessibility and quality. Work with business stakeholders, IT team members and third-party software providers to understand how data can be accessed for analytics from various software systems, and contribute to central inventory of analytics source systems. Continuous improvement of existing data architecture, delivering reusable services and cost saving opportunities in line with the policies and standards of the group. Provide mentoring and coaching for engineers and analysts within the IT and Business Analytics groups. The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. May perform other duties as assigned. Qualifications To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals to perform the essential functions. Bachelor's Degree from a regionally accredited four-year college or university and 8
years of relevant experience, or equivalent combination of education and experience, required. Minimum of 5
years of experience in the design, deployment and management of analytics data processing systems and architectures, required. Minimum of 5
years of experience in an IT leadership role with specific emphasis and deep working knowledge of Data Engineering/Analytics teams; as well as team development and management, required. Expert knowledge of data management, data integration and data platform development techniques Expert knowledge of SQL programming, including demonstrated experience with a combination of functions, stored procedures, cursors, common table expressions, dynamic SQL, data pivoting, query optimization, correlated subqueries, windowing functions, error handling and merge operations Expert knowledge of the Python programming language, with demonstrated experience utilizing Python on the Apache Spark platform Demonstrated experience deploying and utilizing a modern data engineering stack composed of data lake, dimensional data warehouse and Apache Spark-based computer platform Experience deploying and using code version control, CI/CD, error handling and lineage/audit capabilities in analytics data pipelines Deep understanding of the technologies and activities required to develop, deploy and run robust data pipelines to support the derivation of insight from data Experience with cloud-based analytics platforms on AWS and/or Microsoft Azure Experience in Kimball dimensional entity relationship modeling, metadata systems, data quality tools and techniques Ability to relate architectural decisions and recommendations to business needs Experience with data storage technologies such as HDFS-based data lake platforms, Azure Synapse Analytics, MS SQL Server, AWS Redshift, etc. Experience with development of reusable data engineering design patterns Experience managing/mentoring other data engineers Ability to quickly learn and adopt new technology Can work with data to identify patterns, uses judgment to form conclusions that may challenge conventional wisdom, and focuses on the crux of issues to identify high leverage intervention points and strategies Applies original and innovative thinking to produce new ideas and create innovative solutions Ability to translate complex sequences of technical activity into repeatable processes Considers the relative costs and benefits of potential actions to choose the most appropriate one; acts decisively and swiftly Ability to communicate technical concepts in plain language Attention to detail Strong verbal and written communication skills High proficiency with the Microsoft Office suite (Word, Excel, PowerPoint, Visio) Ability to work well with others in a fast-paced, dynamic environment. Ability to be respectful, approachable and team-oriented while building strong working relationships and a positive work environment. Supervisory Responsibilities
Salary Range:
$200K -- $250K
Minimum Qualification
Data Science & Machine Learning, Technology ManagementEstimated Salary: $20 to $28 per hour based on qualifications.
data ingestion, data storage, compute, pipeline orchestration, source code control, error/event handling, CI/CD, access control. Partner with data architects and analysts to develop reusable data pipeline design patterns. Work with IT, business and data management teams to understand data needs and develop solutions. Plan, execute and manage the integration of data across the enterprise's data assets. Develop standards, designs, data maps; developing and modifying functions, programs, routines and stored procedures to export, transform and load data. Collaborate with IT and Business Analytics teams to design and model application data structures, data store and integration processes across a global application landscape. Participate in the selection of emerging technologies for data management, storage, and analytics. Develop and executing test plans and scenarios including data design, tool design, data extract/transform, networks and hardware. Support business case creation. Promote architectural standards and consistency. Assist in assessing solution option risks and develop mitigation plans. Work with business partners to understand requirements for data use, analysis, and quality. Identify and aggregate data from internal and external sources. Coordinate with data owners to ensure data accessibility and quality. Work with business stakeholders, IT team members and third-party software providers to understand how data can be accessed for analytics from various software systems, and contribute to central inventory of analytics source systems. Continuous improvement of existing data architecture, delivering reusable services and cost saving opportunities in line with the policies and standards of the group. Provide mentoring and coaching for engineers and analysts within the IT and Business Analytics groups. The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. May perform other duties as assigned. Qualifications To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals to perform the essential functions. Bachelor's Degree from a regionally accredited four-year college or university and 8
years of relevant experience, or equivalent combination of education and experience, required. Minimum of 5
years of experience in the design, deployment and management of analytics data processing systems and architectures, required. Minimum of 5
years of experience in an IT leadership role with specific emphasis and deep working knowledge of Data Engineering/Analytics teams; as well as team development and management, required. Expert knowledge of data management, data integration and data platform development techniques Expert knowledge of SQL programming, including demonstrated experience with a combination of functions, stored procedures, cursors, common table expressions, dynamic SQL, data pivoting, query optimization, correlated subqueries, windowing functions, error handling and merge operations Expert knowledge of the Python programming language, with demonstrated experience utilizing Python on the Apache Spark platform Demonstrated experience deploying and utilizing a modern data engineering stack composed of data lake, dimensional data warehouse and Apache Spark-based computer platform Experience deploying and using code version control, CI/CD, error handling and lineage/audit capabilities in analytics data pipelines Deep understanding of the technologies and activities required to develop, deploy and run robust data pipelines to support the derivation of insight from data Experience with cloud-based analytics platforms on AWS and/or Microsoft Azure Experience in Kimball dimensional entity relationship modeling, metadata systems, data quality tools and techniques Ability to relate architectural decisions and recommendations to business needs Experience with data storage technologies such as HDFS-based data lake platforms, Azure Synapse Analytics, MS SQL Server, AWS Redshift, etc. Experience with development of reusable data engineering design patterns Experience managing/mentoring other data engineers Ability to quickly learn and adopt new technology Can work with data to identify patterns, uses judgment to form conclusions that may challenge conventional wisdom, and focuses on the crux of issues to identify high leverage intervention points and strategies Applies original and innovative thinking to produce new ideas and create innovative solutions Ability to translate complex sequences of technical activity into repeatable processes Considers the relative costs and benefits of potential actions to choose the most appropriate one; acts decisively and swiftly Ability to communicate technical concepts in plain language Attention to detail Strong verbal and written communication skills High proficiency with the Microsoft Office suite (Word, Excel, PowerPoint, Visio) Ability to work well with others in a fast-paced, dynamic environment. Ability to be respectful, approachable and team-oriented while building strong working relationships and a positive work environment. Supervisory Responsibilities
Salary Range:
$200K -- $250K
Minimum Qualification
Data Science & Machine Learning, Technology ManagementEstimated Salary: $20 to $28 per hour based on qualifications.
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