Supports, develops and maintains a data and analytics platform. Effectively and efficiently process, store and make data available to analysts and other consumers. Works with the Business and IT teams to understand the requirements to best leverage the technologies to enable agile data delivery at scale.
Key Responsibilities:
Implements and automates deployment of our distributed system for ingesting and transforming data from various types of sources (relational, event-based, unstructured). Implements methods to continuously monitor and troubleshoot data quality and data integrity issues. Implements data governance processes and methods for managing metadata, access, retention to data for internal and external users. Develops reliable, efficient, scalable and quality data pipelines with monitoring and alert mechanisms that combine a variety of sources using ETL/ELT tools or scripting languages. Develops physical data models and implements data storage architectures as per design guidelines. Analyzes complex data elements and systems, data flow, dependencies, and relationships in order to contribute to conceptual physical and logical data models. Participates in testing and troubleshooting of data pipelines. Develops and operates large scale data storage and processing solutions using different distributed and cloud based platforms for storing data (e.g. Data Lakes, Hadoop, Hbase, Cassandra, MongoDB, Accumulo, DynamoDB, others). Uses agile development technologies, such as DevOps, Scrum, Kanban and continuous improvement cycle, for data driven application.
Competencies:
System Requirements Engineering - Uses appropriate methods and tools to translate stakeholder needs into verifiable requirements to which designs are developed; establishes acceptance criteria for the system of interest through analysis, allocation and negotiation; tracks the status of requirements throughout the system lifecycle; assesses the impact of changes to system requirements on project scope, schedule, and resources; creates and maintains information linkages to related artifacts.
Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
Communicates effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.
Customer focus - Building strong customer relationships and delivering customer-centric solutions.
Decision quality - Making good and timely decisions that keep the organization moving forward.
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Data Extraction - Performs data extract-transform-load (ETL) activities from variety of sources and transforms them for consumption by various downstream applications and users using appropriate tools and technologies.
Programming - Creates, writes and tests computer code, test scripts, and build scripts using algorithmic analysis and design, industry standards and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.
Quality Assurance Metrics - Applies the science of measurement to assess whether a solution meets its intended outcomes using the IT Operating Model (ITOM), including the SDLC standards, tools, metrics and key performance indicators, to deliver a quality product.
Solution Documentation - Documents information and solution based on knowledge gained as part of product development activities; communicates to stakeholders with the goal of enabling improved productivity and effective knowledge transfer to others who were not originally part of the initial learning.
Solution Validation Testing - Validates a configuration item change or solution using the Function's defined best practices, including the Systems Development Life Cycle (SDLC) standards, tools and metrics, to ensure that it works as designed and meets customer requirements.
Data Quality - Identifies, understands and corrects flaws in data that supports effective information governance across operational business processes and decision making.
Problem Solving - Solves problems and may mentor others on effective problem solving by using a systematic analysis process by leveraging industry standard methodologies to create problem traceability and protect the customer; determines the assignable cause; implements robust, data-based solutions; identifies the systemic root causes and ensures actions to prevent problem reoccurrence are implemented.
Values differences - Recognizing the value that different perspectives and cultures bring to an organization.
Education, Licenses, Certifications:
College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations.
Experience:
Relevant experience preferred such as working in a temporary student employment, intern, co-op, or other extracurricular team activities. Knowledge of the latest technologies in data engineering is highly preferred and includes:
Exposure to Big Data open source
SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent college coursework
Familiarity developing applications requiring large file movement for a Cloud-based environment
Exposure to Agile software development
Exposure to building analytical solutions
Exposure to IoT technology
Core Responsibilities Unique to the Role
Develop and maintain enterprise data products by building data pipelines, data transformations, and curated datasets that support reporting, analytics, automation, and GenAI use cases across Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains.
Apply Data-as-a-Product principles to create reusable, discoverable, and governed data assets with appropriate metadata, lineage, and quality controls, enabling self-service consumption and consistent business outcomes across multiple consumers.
3) Collaborate with Product Managers, Data Engineers, Data Scientists, and Solution Engineers to support delivery of AI-ready data products by ensuring data is reliable, well-structured, and optimized for analytics, machine learning, and GenAI applications.
Required Skills, Education, or Experience
Experience developing and supporting data integration, ETL/ELT processes, data pipelines, and data transformations using modern data platforms and cloud-based technologies.
Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance principles supporting enterprise-scale data products.
Ability to translate business and product requirements into efficient technical solutions while considering scalability, performance, maintainability, and reusability.
Experience working in Agile, cross-functional teams and collaborating effectively with product, engineering, analytics, and business stakeholders.
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Preferred (Nice to Have) Skills, Education, or Experience
Experience working within a Data-as-a-Product operating model, including exposure to data catalogs, lineage, data quality frameworks, and certified data products.
Exposure to AI/ML or GenAI initiatives, including preparation of AI-ready datasets, semantic models, knowledge assets, or data structures supporting intelligent business solutions.
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