Analytics Engineer

We need to output HTML fragment with sections: About the role? Actually the posting includes "About Niche" (company description) and "About The Role". The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — 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. Prefer a few substantive sections over many thin ones. We need to decide which sections to include. The posting includes: About Niche (company description), About The Role (role description), What You Will Do (responsibilities), During The First Month, Within 3 Months, Within 6 Months, Within 12 Months (these could be considered responsibilities or expectations timeline), What We Are Looking For (requirements/qualifications/skills), Compensation (pay), Interview Process (maybe not needed?), Why Niche? (benefits and culture), plus some legal boilerplate at end (equal opportunity, etc.) which we should remove. We need to map to allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can combine sections: "About the role" could contain the role description (About The Role). "Responsibilities" could contain What You Will Do and the timeline expectations (first month, 3 months, etc.) maybe as list items. "Requirements" could contain What We Are Looking For (education, experience, skills). "Skills" could be separate list of specific technical skills. "Benefits" could contain Why Niche? benefits (flexible work, health plan, PTO, stipend, parental leave, 401k, etc.). "Pay" could contain Compensation section (salary range, bonus, stock). "Schedule" maybe not present; we should not create if not present. The posting mentions "Full time, salaried position". That's schedule info (full-time). Could include schedule: Full-time. But we need actual content; we have "Full time, salaried position". That's schedule info. So we can include Schedule section with that. We must not include filler like "commensurate with experience". The compensation section includes "Our national target base salary range is $96,000-$120,000, plus participation in our Annual Bonus and Stock Option Program. Base compensation will be commensurate with experience and skills." The phrase "commensurate with experience and skills" is filler? It's somewhat generic but it's part of the sentence. The instruction says 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. It says we should not create a Pay section if it only contains filler like "commensurate with experience". However we have actual salary range plus bonus/stock. So we can include Pay section with that content, but we might need to remove the filler phrase? The instruction says "never emit a heading followed by filler/placeholder text". It doesn't say we must remove filler within a section; just that we shouldn't have a section that only contains filler. So we can keep the sentence as is. Similarly, Benefits: we have many specific benefits. Good. Schedule: we have "Full time, salaried position". That's specific enough. We also need to include About Niche? Not in allowed headings but maybe we can include as a

before sections? The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but we should follow that pattern. Could include a

for About Niche? Not listed but could be considered "About the role"? Actually About Niche is company overview; maybe we can put that in a

before sections, not as a heading. The instruction: "use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It implies those are the allowed headings; we should not create other headings. So we should not have a heading for About Niche. Instead we can include that content in a

before the first heading, or maybe incorporate into About the role section? But About the role heading is for role description; we could put company description as a

under About the role heading? Might be odd but acceptable. However the instruction says "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it." It doesn't forbid other headings besides those examples, but the examples are given as typical. Safer to stick to those headings only. Thus we will output:

About Niche: Niche is the leader in school search. Our mission is to make researching and enrolling in schools easy, transparent, and free. With in-depth profiles on every school and college in America, 140 million reviews and ratings, and powerful search tools, we help millions of people find the right school for them. We also help thousands of schools recruit more best-fit students, by highlighting what makes them great and making it easier to visit and apply. Niche is all about finding where you belong, and that mission inspires how we operate every day. We want Niche to be a place where people truly enjoy working and can thrive professionally.

Then

About the role

with content: "We are looking for a skilled Analytics Engineer to join the Data Engineering team. Youll build and support scalable data models that translate raw data into clean, business-ready datasets while ensuring data accuracy, documentation, observability, semantic consistency, and optimum performance. Youll be developing and maintaining modular dbt models, semantic layers, and BI views for consumption by business analysts and executive stakeholders. This is an exciting opportunity to join our team as were building the next generation of our analytics platform and semantic capabilities. Youll be reporting to the Manager, Data Engineering (Core)." Then

Responsibilities

with

• items for each bullet from What You Will Do and timeline expectations. We need to preserve all substantive information. Let's extract bullet points from What You Will Do: - Proactive Business Context & Integration: Actively understand evolving business trends, logic, and cross-functional partnerships to proactively embed rich context and governance into the data layer, ensuring metrics are easily discoverable for consistent decision-making - Dimensional Modeling Champion: Develop, document, and maintain robust Kimball dimensional models, semantic definitions, warehouse transformation logic, and BI datasets tailored for business analysts and decision-makers - Semantic Layer and AI Management: Design, build, and maintain clean, version-controlled data models that support semantic layers and support business-wide self-service analytics and reporting - Observability & Quality Control: Ensure high data quality, business logic consistency, and pipeline accuracy through automated dbt testing, lineage tracking, performance tuning, and proactive alerting systems - Process Optimization & Scaling: Analyze and improve complex business logic and fragmented queries, turning disparate analyst requests into standardized, well-documented semantic models - Governance & Security: Integrate essential data governance, metric standardization, and documentation practices, ensuring single-source-of-truth definitions and secure data access across teams Then timeline expectations: During The First Month: - Build relationships with data team members, understand the operating model, and stakeholders that we interact with on a daily basis - Start to learn about our data warehouse structure, transformation pipelines, semantic models, and core business metrics - Start participating in standups, planning, and retrospective meetings - Start delivering on assigned sprint stories and show progress through completed modeling tasks that contribute to team goals Within 3 Months: - Start delivering on assigned analytics engineering tasks to support our semantic layer, reporting needs, and roadmap - Troubleshoot data logic issues, modeling errors, and participating in analytics support activities - Identify areas for improving data transformation processes and semantic definitions, and share with the team Within 6 Months: - Contribute consistently towards building our analytics platform, which includes dbt data models, semantic layers, and BI reporting frameworks - Start to independently own workstreams whether it is periodic analytics engineering activities, or modeling work items in support of our roadmap - Deepen your understanding, and build domain subject matter expertise of our business metrics & data definitions Within 12 Months: - Your contributions have led to us making significant progress in implementing the analytics platform strategy, and key semantic layer initiatives to support company decision-making - Youve established yourself as a key team member with domain subject matter expertise within analytics engineering We can combine these as list items under Responsibilities. Then

  Requirements

  maybe for education and experience: "Bachelors degree in Computer Science, Data Science, Information Systems, or a related field; 3-5 years of experience in analytics engineering, data engineering or data science; Demonstrated experience building, testing, and maintaining production-grade data transformation workflows and semantic models; Practical experience applying dimensional data modeling techniques (Star Schema, Kimball) and using dbt or semantic tools to define, document, and communicate metric logic across teams; Direct experience modeling business domain data (e.g., Salesforce, Google Analytics, ad platforms), product usage metrics, and semi-structured formats like JSON for BI consumption; Software engineering mindset applied to data, leading with the principles of version control (Git), automated testing, CI/CD, data modeling techniques (Dimensional Modeling, Data Vault, and Third Normal Form (3NF), and code documentation; Proficiency in Advanced SQL, dbt, Snowflake, Python, Git, and modern BI tools (e.g., Looker, Tableau); Experience working with Google Analytics, Marketing, Ad & Social media platform, CRM/Salesforce, and BI visualization tools; custom semantic metrics will be a plus; Knowledge and understanding of the modern data stack, and its transformation components - modeling, semantic layers, testing, documentation, governance, and BI integration; Knowledge of dimensional data modeling techniques (Kimball methodology, Star Schema, Snowflake Schema); Self-starter, analytical problem solver, highly attentive to detail, effective communicator, and obsessed with metric standardization and documentation" We could split into Requirements and Skills. But we have both headings allowed. Let's put Requirements for education/experience, and Skills for technical proficiencies. Thus:

  Requirements

  Bachelors degree in Computer Science, Data Science, Information Systems, or a related field. 3-5 years of experience in analytics engineering, data engineering or data science. Demonstrated experience building, testing, and maintaining production-grade data transformation workflows and semantic models. Practical experience applying dimensional data modeling techniques (Star Schema, Kimball) and using dbt or semantic tools to define, document, and communicate metric logic across teams. Direct experience modeling business domain data (e.g., Salesforce, Google Analytics, ad platforms), product usage metrics, and semi-structured formats like JSON for BI consumption. Software engineering mindset applied to data, leading with the principles of version control (Git), automated testing, CI/CD, data modeling techniques (Dimensional Modeling, Data Vault, and Third Normal Form (3NF), and code documentation. Self-starter, analytical problem solver, highly attentive to detail, effective communicator, and obsessed with metric standardization and documentation.