Data Engineer Principals #IN1214

Cummins•Columbus, IN

About The Position

Work with key business stakeholders, IT experts and subject-matter experts to plan, design, build and deliver optimal analytics and data science solutions. Gather requirements and build roadmaps, architectures and lead decision making process for tools selection to help the Product Teams achieve their goals. Collaborate and align with the wider Organization and Enterprise Data Strategy. Create data architectures for data lake, databases, and data warehouse structure based on functional and technical requirements. Create and maintain optimal data pipeline architecture using experience with databases and data management. Apply the science of measurement to assess whether a solution meets its intended outcome using the Function’s defined best practices, including Quality Assurance Metrics and Systems Development Life Cycle (SDLC) standards, tools, metrics and key performance indicators to deliver a quality product. Leverage engineering principles and practices to design, develop, test, and evaluate integrated systems for managing processes such as quality control. Analyze complex business systems, industry requirements, and data regulations. Identify, design and implement internal process improvements including automating manual processes, optimizing data delivery and re-designing infrastructure for greater scalability. Work with Executive, Product, Data and Design teams to assist with data-related technical issues and data infrastructure needs. Create solution designs and patterns using the Cummins Technical Reference Model (CTRM), CLEAN standards, and existing reference patterns to maintain alignment to company standards. Ensure compliance with data governance and data security requirements while creating, improving and operationalizing these integrated and reusable data pipelines, enabling faster data access, integrated data reuse and vastly improved time-to-solution for the data and analytics initiatives. Develop applications for a Cloud-based environment and data extraction tools from variety of sources using experience with Big Data technologies including Java, Map-Reduce, SPARK, Scala, HBase, Hive, Kafka, ODBC, and SQL query language. Drive failure mode, troubleshooting methods, and data quality across operational business processes and decision making. Work with data and analytics experts to strive for greater functionality in data systems. Apply experience with IoT Technology in manufacturing, end products, and other applications where sensors capture and generate large amounts of data that need to be transferred using data pipelines to massive data lakes that are powered and managed by Big Data technologies. Utilize Agile software development methodologies including DevOps, Scrum, and Kanban to manage various projects including software development and advanced analytics product development to increase productivity and deliver products that meet value to customers.

Requirements

Experience with databases and data management
Experience with Big Data technologies including Java, Map-Reduce, SPARK, Scala, HBase, Hive, Kafka, ODBC, and SQL query language.
Experience with IoT Technology
Experience with Agile software development methodologies including DevOps, Scrum, and Kanban

Responsibilities

Work with key business stakeholders, IT experts and subject-matter experts to plan, design, build and deliver optimal analytics and data science solutions.
Gather requirements and build roadmaps, architectures and lead decision making process for tools selection to help the Product Teams achieve their goals.
Collaborate and align with the wider Organization and Enterprise Data Strategy.
Create data architectures for data lake, databases, and data warehouse structure based on functional and technical requirements.
Create and maintain optimal data pipeline architecture using experience with databases and data management.
Apply the science of measurement to assess whether a solution meets its intended outcome using the Function’s defined best practices, including Quality Assurance Metrics and Systems Development Life Cycle (SDLC) standards, tools, metrics and key performance indicators to deliver a quality product.
Leverage engineering principles and practices to design, develop, test, and evaluate integrated systems for managing processes such as quality control.
Analyze complex business systems, industry requirements, and data regulations.
Identify, design and implement internal process improvements including automating manual processes, optimizing data delivery and re-designing infrastructure for greater scalability.
Work with Executive, Product, Data and Design teams to assist with data-related technical issues and data infrastructure needs.
Create solution designs and patterns using the Cummins Technical Reference Model (CTRM), CLEAN standards, and existing reference patterns to maintain alignment to company standards.
Ensure compliance with data governance and data security requirements while creating, improving and operationalizing these integrated and reusable data pipelines, enabling faster data access, integrated data reuse and vastly improved time-to-solution for the data and analytics initiatives.
Develop applications for a Cloud-based environment and data extraction tools from variety of sources using experience with Big Data technologies including Java, Map-Reduce, SPARK, Scala, HBase, Hive, Kafka, ODBC, and SQL query language.
Drive failure mode, troubleshooting methods, and data quality across operational business processes and decision making.
Work with data and analytics experts to strive for greater functionality in data systems.
Apply experience with IoT Technology in manufacturing, end products, and other applications where sensors capture and generate large amounts of data that need to be transferred using data pipelines to massive data lakes that are powered and managed by Big Data technologies.
Utilize Agile software development methodologies including DevOps, Scrum, and Kanban to manage various projects including software development and advanced analytics product development to increase productivity and deliver products that meet value to customers.