Supply Chain Analytics Engineer II

Join DICK'S Sporting Goods onsite in Coraopolis, PA to build governed supply chain analytics and AI-enabled data products.

Responsibilities

• Partner with business stakeholders, Product, and Data Engineering teams to understand Supply Chain operations, business processes, system workflows, and data requirements
• Convert business needs into scalable analytics solutions aligned with business objectives and operational decision-making
• Design, build, maintain, and enhance curated datasets, semantic models, and reusable analytical products for trusted reporting, self-service analytics, and AI-enabled solutions
• Use SQL, data transformation techniques, business logic, and data modeling practices to integrate, validate, and transform complex data into governed, business-ready information assets
• Develop and maintain semantic layers, certified datasets, governed metrics, and business definitions to improve consistency, reduce duplication, and increase data accessibility
• Prepare and optimize analytical data products and data experiences for AI-enabled and conversational analytics and predictive insights
• Support AI-enabled analytics use cases through semantic preparation, prompt development, and output validation with relevant technical and governance teams
• Apply established standards for data quality, performance, and governance across analytical data products
• Execute test plans, validate source-to-target data flows, assess data quality, and validate analytical solutions using established testing methodologies
• Document, communicate, and resolve identified issues and reliability concerns
• Maintain business and technical metadata including source-to-target mappings, data lineage, ownership information, technical specifications, and documentation in approved data catalog and knowledge management tools
• Apply enterprise requirements for data classification, privacy, security, access control, and retention across analytical datasets and semantic models
• Coordinate with security, privacy, and governance teams to ensure compliance with established standards
• Monitor production data products, triage data quality and reliability incidents, support recovery and escalation, document root causes, and implement corrective and preventive improvements
• Assess upstream and downstream impacts of changes to data, schema, metrics, systems, and processes
• Coordinate testing, stakeholder communication, and implementation activities to keep analytical assets accurate and aligned with changing needs
• Collaborate across Operations, Finance, Product, Data Engineering, Software Engineering, and Business Intelligence to design, deliver, and maintain data assets
• Support vendor and operational data integrations (e.g., FTP, Kafka, DataStage) to ensure reliable delivery, quality, and availability
• Assist with implementation of new data sources and elements by supporting requirements gathering, solution design, testing, deployment, and change management
• Provide technical guidance and knowledge transfer to downstream analytics teams on data models, semantic layers, governed metrics, data availability, and structural changes
• Maintain awareness of evolving analytics, AI-enabled analytics, business intelligence, and data management practices; apply new skills to improve team capabilities

Requirements

• Bachelor's degree (BS) in Computer Science, Information Systems, Data Analytics, Engineering, Statistics, Supply Chain, or a related field
• Analytics engineering, business intelligence, data management, data engineering, or a related discipline
• Experience including proficient SQL, multi-source data transformation, dimensional or semantic modeling, data-quality testing, documentation, production support, and delivery of analytical data products
• Ability to understand business processes and system workflows and connect them to how data is generated, transformed, and consumed
• Supply-chain or Manhattan-system experience is preferred
• Relevant training or certifications in SQL, analytics engineering, cloud data platforms, data governance, BI tooling, or AI-enabled analytics are preferred
• Experience working with cloud-based data and analytics platforms (e.g., Databricks, Snowflake, Azure, AWS, Google Cloud)
• Experience supporting ingestion/integration/orchestration using technologies such as Kafka, FTP, ETL/ELT frameworks, or similar tools
• Experience developing datasets and analytical solutions using business intelligence and self-service analytics platforms (e.g., Cognos, Power BI, Qlik)
• Familiarity with metadata management, data lineage, documentation, and data catalog practices supporting data quality and governance
• Experience with version control tools (e.g., Git) and applying testing, deployment, change management, and work management practices (e.g., Jira)
• Familiarity with AI-enabled analytics, conversational analytics, predictive analytics, and related technologies

Technologies

• SQL
• Kafka
• FTP
• DataStage
• ETL/ELT
• Databricks
• Snowflake
• Azure
• AWS
• Google Cloud
• Cognos
• Power BI
• Qlik
• Git
• Jira

Virtual Requirements

• Candidates do not use AI tools (like ChatGPT or others) during interviews or assessments
• Cameras must be on during all virtual interviews
• AI tools are not permitted to be used by the candidate during any part of the interview process
• Offers are contingent upon a satisfactory background check which may include ID verification