Sr. Engineer, Data & Analytics

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use

for each heading. Only include sections that have real specific content. Must not repeat job title. Must not include Job ID etc. Remove boilerplate. Must preserve all substantive info. We need to parse the job posting. First part: About Lovesac (company description) – not needed as a section? Could be part of About the role? The instruction: Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can include About the role heading with content about the role (the "About The Role" paragraph). Then Responsibilities: list of duties (What You'll Do). Then Requirements: maybe the bullet list under Requirements? Actually there is a "Qualifications" section with subheadings: Who you Are: Core Values..., Table-Stake Values..., Core Competencies..., then Requirements bullet list. The instruction says we can have Qualifications and Skills sections. Might combine core competencies as Skills? Or we can have Qualifications (education, experience) and Skills (technical skills). Let's decide. We need to include sections only if posting has real specific content. We have: - About The Role: paragraph describing role. - What You'll Do: list of duties (multiple bullet-like paragraphs). We'll turn into

• . - Requirements: bullet list starting with "Bachelor’s degree..." etc. That's qualifications. - Also there is a "Qualifications" section with core values etc. That could be considered Skills or Qualifications? The core values and competencies are more like behavioral traits; could be placed under Skills or Qualifications. But we already have Requirements for education/experience. We could put core values/competencies under Skills. - Benefits: there is a "Full Time Benefits*" section with financial, health, PTO etc. That's specific content. So include Benefits. - Pay: there is a line: "Associate pay will vary based on factors such as qualifications, experience, skill level and competencies." That's vague but it's actual statement about pay. It says pay will vary based on factors. That's not a specific number or range, but it's still content about pay. The instruction: 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. The phrase "Associate pay will vary based on factors such as qualifications, experience, skill level and competencies." is essentially "varies". That's placeholder-ish. It says pay will vary based on factors. That's similar to "commensurate with experience". Likely we should NOT include Pay section because it's not specific. The instruction says never create Pay section unless posting actually states them. It does state that pay will vary based on factors, but that's not a concrete statement. I'd interpret that as filler and avoid Pay section. - Schedule: there is mention: "This position is remote and will report into Lovesac Corporate HUB based in Stamford, CT. Candidates must fully reside in the United States at all times during employment and should have the ability to travel as needed." That's about location/travel, not schedule. No explicit schedule (full-time/part-time, hours). There's mention of Full Time Benefits, implying full-time. But schedule not explicitly stated. So we should not include Schedule. Thus sections: About the role, Responsibilities, Requirements, Skills (maybe from core values/competencies), Benefits. We need to ensure we don't repeat job title. Use

  for each. Now extract content. About the role: The paragraph under "About The Role": "As our Sr. Engineer, Data & Analytics, you will build both the data foundation and the analytics on top of it—designing and scaling the pipelines, models, and Power BI solutions that turn raw data into trusted, actionable insight across Lovesac’s business. Working primarily in Microsoft Fabric, you will engineer end-to-end data flows (ingestion, transformation, and modeling) and deliver high-impact Power BI dashboards and semantic models that support customer experience, store performance, merchandising, supply chain, and omnichannel growth. You’ll partner closely with Data Engineering, Technology, Merchandising, Marketing, Store Operations, Supply Chain, and Corporate teams to ensure data is trusted, accessible, and decision-ready for both corporate and field leaders. This role blends deep technical expertise with strong business acumen and a passion for enabling data-driven decisions. This position is remote and will report into Lovesac Corporate HUB based in Stamford, CT. Candidates must fully reside in the United States at all times during employment and should have the ability to travel as needed." We should keep that as prose

  . Responsibilities: The "What You’ll Do" section includes multiple bullet-like paragraphs. We'll convert each sentence or clause into

  . Let's list them: - Power BI Engineering & Analytics Delivery - Design, develop, and maintain scalable, high-performance Power BI dashboards, reports, and apps aligned to business needs. - Build and optimize Power BI semantic models, leveraging best practices in data modeling, DAX, and performance tuning. - Translate complex business questions into intuitive, easy-to-use visualizations that drive insight and action. - Partner with stakeholders to define requirements, success metrics, and reporting standards. - Data Engineering & Pipelines (Microsoft Fabric) - Design, build, and maintain scalable data ingestion and ETL/ELT pipelines in Microsoft Fabric (Data Factory pipelines, Dataflows Gen2, and Spark notebooks) to land data from source systems into OneLake. - Develop and optimize Lakehouse and Warehouse solutions in Fabric, applying a medallion (bronze/silver/gold) architecture with Delta/Parquet to produce clean, reliable, analytics-ready data. - Write efficient transformation logic in SQL, Python, and PySpark, and tune Spark and warehouse workloads for performance and cost. - Orchestrate, schedule, and monitor pipelines end-to-end, building proactive alerting and error handling so data-refresh failures are caught and resolved before they reach the business. - Connect the engineering layer directly to Power BI using Direct Lake and well-structured star schemas, minimizing data movement and duplication. - Data Modeling & Integration - Own and shape source data models, ETL/ELT logic, and data pipelines—partnering with Platform and Data Engineering—to support reliable, governed reporting. - Ensure analytics solutions are built on clean, well-structured, and governed data. - Identify opportunities to improve data quality, usability, and scalability across reporting layers. - Establish governed, reusable data products in OneLake that serve as a single source of truth across Power BI and downstream analytics. - Analytics Enablement & Adoption - Act as a trusted analytics partner to business teams, guiding them on how to interpret and use insights effectively. - Ensure dashboards and reports are actionable, clearly documented, and optimized for ongoing adoption. - Embed insights into day-to-day decision-making across corporate and field teams. - Governance, Standards & Best Practices - Establish and follow Power BI standards for modeling, visualization, performance, and security. - Implement row-level security (RLS) and follow data privacy and governance requirements. - Maintain consistent definitions, metrics, and reporting logic to ensure a single source of truth. - Apply source control and CI/CD using Git integration and Fabric deployment pipelines to promote changes safely across development, test, and production. - Manage workspace access, sensitivity labels, and data governance across Fabric and Power BI (e.g., Microsoft Purview) to keep data secure and compliant. - Continuous Improvement & Innovation - Stay current on Power BI, Microsoft Fabric, and analytics best practices. - Identify opportunities to enhance existing dashboards, automate reporting, and introduce advanced analytics capabilities. - Serve as a technical mentor and thought partner to others across analytics and data teams. - Measurement & Impact - Ensure analytics solutions drive measurable business impact, adoption, and decision quality. - Track usage, performance, and effectiveness of dashboards to continuously improve value delivery. - Partner with stakeholders to ensure insights translate into meaningful action. - Perform any other reasonable duties for this role as requested by management. We'll combine into a single

  with
  • items. Requirements: The bullet list under "Requirements" (starting with Bachelor’s degree). Let's extract each line: - Bachelor’s degree in Computer Science, Data Science, Engineering, Analytics or related field required. - 5+ years of experience across data engineering and analytics/BI, with hands-on expertise in Power BI and in building production data pipelines. - Strong experience delivering data and AI solutions that drive business outcomes. - Proven experience using Power BI platforms, driving scalable analytics and enterprise insights. - Advanced experience with DAX, data modeling, performance optimization, and visualization best practices. - Strong SQL skills and experience working with modern data platforms (e.g., Microsoft Fabric, Azure, Snowflake, Databricks, Redshift, BigQuery). - Hands-on experience with Microsoft Fabric—OneLake, Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, Notebooks, and Direct Lake—or an equivalent modern data engineering stack. - Proven data engineering experience designing, building, and orchestrating ETL/ELT pipelines and data ingestion at scale. - Proficiency in Python and PySpark (or Spark SQL) for large-scale data transformation. - Experience with a lakehouse/medallion architecture (bronze/silver/gold) and Delta Lake/Parquet. - Familiarity with Git-based source control and CI/CD or deployment pipelines for analytics and data assets. - Experience partnering with business leaders to deliver analytics that drive business decisions. - Strong communication skills with the ability to translate technical concepts into business-friendly insights. - Self-directed, curious, and driven by continuous improvement. - Experience in furniture, home goods, lifestyle, or other high-consideration consumer categories, preferred. - Exemplify each of our Lovesac values, at all times, be results driven and utilize knowledge to meet or exceed key performance indicators (KPIs), goals and deadlines. - Must be able to travel using various forms of transportation, as required by the Company in its sole discretion, for mandatory meetings and conferences held either at our offices or off-site (i.e. quarterly team connection weeks, companywide meetings and events, vendor visits). - Must comply with all policies and procedures outlined in the Lovesac Employee Handbook and work collaboratively with fellow employees, treating all clients, both internal and external with dignity and respect at all times. We'll put these in
    • . Skills: Could be derived from core values/competencies etc. The "Qualifications" section includes: Who you Are: Core Values: Top Ambition, We All Win Together, Conscious Operations, Do Less and Do Best, Love Matters Table-Stake Values: Willing to Sweep Floors, Grit, Positive, Self-Aware, Self-Starting, Insatiable Learners, Transparency, Customer-Centric Core Competencies: Builds Customer Centricity, Drives Remark-able Results, Collaborates Effectively, Makes Good Decisions, Demonstrates Self-Awareness These are more like behavioral traits/skills. We'll put them under Skills heading as a list. We could also include technical skills from Requirements but those are already in Requirements. Better to separate: Requirements = education/experience; Skills = core values/competencies. Thus Skills section: list each item. We need to preserve all substantive information; we should not lose any. So we need to include all core values and competencies. Let's list them: Core Values: - Top Ambition - We All Win Together - Conscious Operations - Do Less and Do Best - Love Matters Table-Stake Values: - Willing to Sweep Floors - Grit - Positive - Self-Aware - Self-Starting - Insatiable Learners - Transparency - Customer-Centric Core Competencies: - Builds