Data Engineer - Senior 1

Job Summary:

Leads projects for design, development and maintenance of a data and analytics platform. Effectively and efficiently process, store and make data available to analysts and other consumers. Works with key business stakeholders, IT experts and subject-matter experts to plan, design and deliver optimal analytics and data science solutions. Works on one or many product teams at a time.

Key Responsibilities:

Designs and automates deployment of our distributed system for ingesting and transforming data from various types of sources (relational, event-based, unstructured). Designs and implements framework 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. Designs and provide guidance on building 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. Designs and implements physical data models to define the database structure. Optimizing database performance through efficient indexing and table relationships. Participates in optimizing, testing, and troubleshooting of data pipelines. Designs, 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 innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. Assists with renovating the data management infrastructure to drive automation in data integration and management. Ensures the timeliness and success of critical analytics initiatives by using agile development technologies such as DevOps, Scrum, Kanban Coaches and develops less experienced team members.

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.

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:
Intermediate experience in a relevant discipline area is required. Knowledge of the latest technologies and trends in data engineering are highly preferred and includes:
- Familiarity analyzing complex business systems, industry requirements, and/or data regulations
- Background in processing and managing large data sets
- Design and development for a Big Data platform using open source and third-party tools
- SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent college coursework
- SQL query language
- Clustered compute cloud-based implementation experience
- Experience developing applications requiring large file movement for a Cloud-based environment and other data extraction tools and methods from a variety of sources
- Experience in building analytical solutions
Intermediate experiences in the following are preferred:
- Experience with IoT technology
- Experience in Agile software development

Core Responsibilities Unique to the Role

1) Design, build, and optimize reusable data pipelines, curated data assets, and domain-aligned data products that support analytics, operational reporting, APIs, automation, and GenAI use cases across Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains.
2) Apply Data-as-a-Product principles by developing scalable, governed, and discoverable data assets with appropriate metadata, lineage, quality controls, and documentation, enabling self-service consumption and enterprise-wide reuse.
3) Partner with Product Managers, Data Scientists, Solution Engineers, and business stakeholders to prepare and deliver AI-ready datasets, semantic models, vectorized content, and trusted knowledge sources that support GenAI, advanced analytics, and intelligent business solutions.

Required Skills, Education, or Experience

1) Strong hands-on experience developing and supporting enterprise data pipelines, data transformations, and data integration solutions using modern cloud data platforms, data warehouses, and ETL/ELT technologies.
2) Experience with data modeling, SQL development, data quality validation, performance optimization, and scalable data architecture supporting multiple consumer patterns including reporting, APIs, analytics, and AI/ML.
3) Ability to translate business and product requirements into reusable technical solutions while balancing data quality, performance, maintainability, and long-term scalability.
4) Experience working within cross-functional Agile teams and collaborating effectively with Product Managers, Data Scientists, Architects, and business stakeholders to deliver business outcomes.
5) Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.

Preferred (Nice to Have) Skills, Education, or Experience

1) Experience working within a Data-as-a-Product operating model, including data cataloging, lineage, metadata management, data product certification, and governance practices.

2) Exposure to GenAI, AI/ML, retrieval-augmented generation (RAG), vector databases, semantic search, knowledge management, or AI-ready data engineering practices supporting enterprise AI solutions.