Senior Data Engineer, Supply Chain & AI

We need to output HTML fragment with sections: About the role? The posting includes description paragraphs. We need to use

for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections when posting has real specific content. We need to parse content: - About the role: The first paragraph(s) describing team description and role description). Could be "About the role". We'll include that. - Responsibilities: The "What you’ll be doing:" bullet list (actually sentences). We'll extract responsibilities list. - Requirements: The "What you bring to the table:" maybe qualifies as skills? Actually "What you bring to the table" is more like skills/qualities. Then "What’s needed- Basic Qualifications:" and "Preferred Qualifications:" are qualifications. - Skills: Could be extracted from responsibilities? But we can have a Skills section summarizing technical skills? However we must not invent content. We can include a Skills section if posting explicitly lists skills. The posting includes "Advanced SQL skills", "Production experience with Snowflake", "Production experience with dbt", "Hands-on programming experience with Python", etc. Those are qualifications. Could be placed under Requirements or Qualifications. The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have multiple sections. We need to decide which content goes where. We have: - About the role: intro paragraphs. - Responsibilities: bullet list of duties (the "What you’ll be doing:" section). - Requirements: maybe the "What you bring to the table:" (mindset, communication, etc.)? Or could be considered Skills. - Qualifications: Basic and Preferred qualifications. - Skills: maybe we can extract technical skills from responsibilities and qualifications? But we must not invent. Could create a Skills section listing specific technical skills mentioned: Snowflake, dbt, Python, PySpark/Spark, Databricks, Unity Catalog, Genie, Azure/GCP, Airflow, ADF, Kafka, IaC, containers, APIs, event schemas, dimensional modeling, data vault, MDM, governance, privacy, RBAC. However those are in qualifications. Might be better to put them under Qualifications (Basic/Preferred). The instruction says we can have Qualifications section. So we can have Qualifications with subpoints. - Benefits: The "We Offer:" section includes inclusive culture, PTO, discounts, 401k, wellness programs. Also mentions salary range but not specific numbers. We should not create Pay section unless posting actually states them. It says "The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced..." That's not a specific number, it's vague. According to rule: never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. For Benefits, we have concrete benefits: 22 days PTO, holiday schedule, discounts, 401k match, wellness programs. That's specific enough. So we can include Benefits section. - Pay: Not specific numbers, so we should NOT include Pay section. - Schedule: Not mentioned, so omit. Thus sections: About the role, Responsibilities, Requirements (maybe combine "What you bring to the table" as Requirements?), Qualifications (Basic and Preferred), Benefits. We could also have Skills section separate from Requirements? But we can incorporate skills into Qualifications. However the instruction list includes Skills as possible heading; we can omit if not needed. We need to use

• for any list of duties/requirements/benefits. So for Responsibilities we need a list. For Requirements we could list bullet points. For Qualifications we could list bullet points (maybe separate Basic and Preferred as subheadings? But we cannot use headings other than h3? We could embed or just list with labels. Probably okay to have plain text within
• . We must preserve ALL substantive information; do not invent or summarize away detail. So we need to keep all details from the posting, but we can rephrase? Must preserve meaning, not summarizing away detail. We can keep sentences as is, maybe break into bullet points but keep content. We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. There's none obvious besides maybe the "We Offer:" includes some generic but we keep. We need to output only HTML fragment. Let's craft:

  About the role

  The Staples Enterprise Data team builds and governs scalable data products that enable trusted, timely decision-making. Within Data & Analytics, the Supply Chain Data team partners across transportation, fulfillment, inventory, delivery, returns, product, analytics, and engineering to create dependable foundations for operational reporting, advanced analytics, and AI-enabled experiences. As a Senior Data Engineer I, you will design, build, test, deploy, and operate modern data products for Staples Supply Chain. You will translate complex operational needs into governed, reusable datasets and pipelines, with Snowflake and dbt serving as the primary production stack. You will also help extend the platform for AI-ready data products and conversational analytics using Snowflake and Databricks capabilities where they provide clear value. In this hands-on role, you will own solutions from design through production, build strong cross-functional partnerships, and improve reliability, data quality, security, and measurable business outcomes. Your work will help supply chain teams find and trust the data they need to make faster, better-informed decisions while enabling future analytics and AI experiences.

  Responsibilities

  • Design, develop, test, and support batch, event-driven, and near-real-time pipelines for supply chain data ingestion, integration, transformation, and curation.
  • Build modular, well-documented dbt models in Snowflake using reusable patterns, incremental processing, testing, lineage, and performance-conscious design.
  • Create governed, analytics-ready data products that support transportation, fulfillment, inventory, delivery promise, last-mile delivery, returns, and related supply chain decisions.
  • Partner with supply chain product owners, analysts, application teams, data scientists, and business stakeholders to clarify requirements, define data contracts, and deliver fit-for-purpose solutions.
  • Build reliable data foundations for AI and agentic workflows by creating governed datasets, semantic context, metadata, evaluation data, and secure production interfaces.
  • Use Databricks capabilities such as Spark, notebooks, workflows, Unity Catalog, and Genie when they are the right fit for scalable engineering, governed data products, and conversational analytics.
  • Implement automated data quality checks, unit and integration tests, reconciliation controls, observability, alerting, and production support practices.
  • Optimize Snowflake compute, storage, SQL, and dbt workloads for performance, reliability, and responsible cost management.
  • Apply software engineering practices including Git-based version control, peer review, CI/CD, environment promotion, secrets management, and infrastructure automation.
  • Create reusable frameworks and components while reducing technical debt and improving engineering standards across the team.
  • Document architecture, data models, lineage, operational procedures, and support expectations so solutions are transparent and maintainable.
  • Lead assigned initiatives from design through production with minimal oversight, communicate risks early, and contribute actively to design and code reviews.

  Requirements

  A strong ownership mindset and the ability to move from ambiguous business needs to practical, supportable data solutions. Clear written and verbal communication, with the ability to explain technical choices, tradeoffs, risks, and outcomes to technical and nontechnical partners. Curiosity, sound judgment, and a continuous-learning mindset in a fast-moving data and AI environment. The ability to balance delivery speed with quality, governance, security, and long-term maintainability. Comfort working independently while collaborating across product, engineering, analytics, and business teams.

  Qualifications

  Basic Qualifications

  • Bachelor's degree in computer science, engineering, information systems, data analytics, or a related quantitative field, or equivalent practical experience.
  • 7+ years of hands-on data engineering or analytics engineering experience delivering production data solutions, or equivalent experience demonstrated through progressively complex technical ownership.
  • Experience working with supply chain data or closely related operational domains, with working knowledge of one or more areas such as transportation, fulfillment, inventory, order lifecycle, delivery, returns, reverse logistics, or distribution operations.
  • Advanced SQL skills and hands-on experience designing scalable data models, ELT pipelines, and cloud data warehouse solutions.
  • Production experience with Snowflake, including data modeling, workload optimization, access patterns, and operational support.
  • Production experience with dbt, including modular model design, tests, documentation, lineage, incremental strategies, and deployment practices.
  • Hands-on programming experience with Python and working knowledge of PySpark or Apache Spark for distributed data processing.
  • Experience with Microsoft Azure or Google Cloud, including cloud storage, identity and access patterns, orchestration, monitoring, and secure integration with modern data platforms.
  • Experience implementing data quality, monitoring, troubleshooting, root-cause analysis, and support controls for business-critical pipelines.
  • Experience delivering multiple concurrent initiatives in an Agile product or engineering environment.

  Preferred Qualifications

  • Hands-on experience with Databricks, including Spark, Delta Lake, Unity Catalog, notebooks, jobs or workflows, and governed data-product development.
  • Experience enabling Databricks Genie or Genie Code, semantic layers, natural-language analytics, or AI-assisted data engineering workflows.
  • Experience building data foundations for machine learning, generative AI, retrieval, or agentic applications, including metadata, data contracts, evaluation, and production monitoring.
  • Experience with orchestration and streaming technologies such as Apache Airflow, Azure Data Factory, Kafka, or cloud-native equivalents.
  • Experience with infrastructure as code, containerized workloads, APIs, event schemas, and secure integration patterns.
  • Knowledge of dimensional modeling, data vault or domain-oriented data products, master data, data governance, privacy, and role-based access control.
  • Experience mentoring engineers, improving standards, and influencing technical direction through design reviews and reusable patterns.
  • Background in retail, B2B, B2C, ecommerce, logistics, or high-volume operational data environments.

  Benefits

  • Inclusive culture with associate-led Business Resource Groups
  • 22 days of PTO and Holiday Schedule (7 observed paid holidays + 1 floating holiday)
  • Online and Retail Discounts
  • Company Match 401(k)
  • Physical and Mental Health Wellness programs, and more!
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