Supply Chain Analytics Engineer II

At DICK’S Sporting Goods , we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams.  We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.

If you are ready to make a difference as part of the world’s greatest sports team, apply to join our team today!

OVERVIEW:

The Analytics Engineer II bridges business operations, Data Engineering, and Business Intelligence by applying knowledge of Supply Chain processes, system workflows, and data relationships to develop trusted data products, scalable semantic models, and AI-enabled analytical capabilities. The role is responsible for the transformation, modeling, quality, documentation, delivery of governed business-ready data assets that provide a trusted foundation for reporting, self-service analytics, operational decision-making, and AI-enabled solutions. The role partners with Data Engineering on data ingestion and platform capabilities and supports downstream analytics teams through reliable data products, governed definitions, and technical knowledge transfer.

Data Products & Analytics Solutions :

- Partner with business stakeholders, Product, and Data Engineering teams to understand Supply Chain operations, business processes, system workflows, and data requirements. Translate business needs into scalable analytics solutions that support operational decision-making and align with business objectives.
- Design, build, maintain, and enhance curated datasets, semantic models, and reusable analytical products that provide a trusted foundation for reporting, self-service analytics, operational decision-making, and AI-enabled solutions.
- Apply SQL, data transformation techniques, business logic, and data modeling practices to integrate, validate, and transform complex data into trusted, governed, and business-ready information assets that accurately reflect business processes and system workflows.
- Develop and maintain semantic layers, certified datasets, governed metrics, and business definitions that promote reporting consistency, improve data accessibility, and reduce duplication across analytics solutions.
- Prepare and optimize analytical data products, semantic models, governed business definitions, and data experiences that support AI-enabled and conversational analytics, predictive insights, and other emerging data-driven capabilities. Contribute to AI-enabled analytics use cases through semantic preparation, prompt development, and output validation, in partnership with appropriate technical and governance teams.

Data Quality, Governance & Reliability:

- Apply established standards for data quality, performance, and governance across analytical data products. Leverage business process, system, and technical knowledge to execute test plans, validate source-to-target data flows, assess data quality, and validate analytical solutions. Document, communicate, and resolve identified issues using standardized testing methodologies.
- Maintain business and technical metadata, source-to-target mappings, data lineage, ownership information, technical specifications, and supporting documentation within approved data catalog and knowledge management tools to improve discoverability, auditability, governance, and change-impact analysis.Apply enterprise data-classification, privacy, security, access-control, and retention requirements to analytical datasets, semantic models, and self-service analytics solutions. Partner with appropriate security, privacy, and governance teams to ensure compliance with established standards.
- Monitor assigned data products in production, triage data quality and reliability incidents, support recovery and escalation activities, communicate identified business impact, document root causes, implement corrective actions and contribute to preventive reliability improvements.
- Assess upstream and downstream impacts of data, schema, metric, system, and process changes. Coordinate testing, stakeholder communication, and implementation activities to ensure analytical assets remain accurate, reliable, and aligned with evolving business and system requirements.

Business & Technical Partnership:

- Develop effective working relationships with business partners to understand business processes, system workflows, data needs, and analytical challenges.
- Collaborate with Operations, Finance, Product, Data Engineering, Software Engineering, and Business Intelligence teams to support the design, delivery, and maintenance of data assets.
- Partner with internal and external teams to identify and capture new data requirements, perform root-cause analysis, and support the diagnosis and resolution of data-related issues.

Data Integration & Lifecycle Support:

- Monitor and support vendor, product, and operational data integrations (e.g. FTP, Kafka, DataStage) to ensure reliable data delivery, data quality, and availability of analytical data products.
- Collaborate with Product, Data Engineering, and technology teams on development, testing, incident resolution, and monitoring activities related to data integrations and processing workflows.
- Participate in the implementation of new data sources and data elements by supporting requirements gathering, solution design, testing, deployment, and change management activities.

Team Practices & Continuous Improvement :

- Provide technical guidance and knowledge transfer to downstream analytics teams regarding data models, semantic layers, governed metrics, data availability, and structural changes.
- Build knowledge of Supply Chain operations, business processes, system workflows, and data structures to effectively support analytical solutions and operational decision-making.
- Share knowledge and provide guidance to less experienced teammates in data analysis, troubleshooting, and analytics development activities. Support assigned planning, delivery, and continuous improvement activities.
- Maintain awareness of evolving analytics, AI-enabled analytics, business intelligence, and data management practices, applying new skills and knowledge to improve analytical solutions and team capabilities.

QUALIFICATIONS:

- 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 should include proficient SQL, multi-source data transformation, dimensional or semantic modeling, data-quality testing, documentation, production support, and delivery of analytical data products.
- Demonstrated ability to understand business processes and system workflows and connect them to how data is generated, transformed, and consumed is required.
- 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.
- Proficiency in SQL, data transformation techniques, and data modeling concepts used to integrate, validate, and prepare data for analytical solutions
- Experience working with cloud-based data and analytics platforms (e.g. Databricks, Snowflake, Azure, AWS, Google Cloud) to support data analysis and reporting needs.
- Experience supporting data ingestion, integration, and orchestration processes 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 that promote data quality, discoverability, and governance.
- Experience using version control tools (e.g., Git) and applying testing, deployment, and change management, and work management practices (e.g. Jira) within the analytics development lifecycle.
- Familiarity with AI-enabled analytics, conversational analytics, predictive analytics, and related technologies used to support data-driven decision-making.

VIRTUAL REQUIREMENTS:

At DICK’S, we thrive on innovation and authenticity. That said, to protect the integrity and security of our hiring process, we ask that candidates do not use AI tools (like ChatGPT or others) during interviews or assessments.

To ensure a smooth and secure experience, please note the following:

- 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 .

If you have any questions or need accommodations, we’re here to help. Thanks for helping us keep the process fair and secure for everyone!