Analytics Engineer

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. About The Role We are looking for a skilled Analytics Engineer to join the Data Engineering team. You’ll 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. You’ll 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 we’re building the next generation of our analytics platform and semantic capabilities. You’ll be reporting to the Manager, Data Engineering (Core). 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 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 Start troubleshooting 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 You’ve established yourself as a key team member with domain subject matter expertise within analytics engineering What We Are Looking For Bachelor’s 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 (