Staff Analytics Engineer

<div class="content-intro"><p>At MNTN, we put our people first, full stop. This allows our company culture to be defined by our team members and their shared values, like trust, ambition, quality, radical honesty, and compassionate leadership. It’s why we all <em data-stringify-type="italic">really</em> love working for the Hardest Working Software in Television™ (and also why we were named one of Ad Age’s Best Places To Work in 2025.)</p> <p>We pride ourselves on bringing unrivaled performance and simplicity to Connected TV advertising. Our self-serve technology makes running TV ads as easy as search and social, helping brands drive measurable conversions, revenue, site visits, and more. It’s what led MNTN to being named one of Fast Company's Most Innovative Companies in 2023. You can learn more about us and everything we do by visiting <a href="https://mountain.com/">https://mountain.com/</a>.</p> <p>We’re committed to innovation that empowers, not replaces. At MNTN, AI is a tool for growth, enhancing efficiency while keeping a people-first approach. Our goal is to streamline workflows and drive new solutions—without compromising the human element that makes our company great.</p> <p>So if wanting to do more, own more, and make a bigger impact comes naturally to you, then you may be the person we're looking for to join us in our next stage of growth.</p></div><p>We're looking for our very fist Staff Analytics Engineer to help scale the foundation that powers decision-making across Product, Engineering, Finance, Revenue, and Executive leadership. This is a highly technical individual contributor role that combines data modeling, architecture, analytics engineering, and cross-functional leadership. You'll work at the intersection of business context and data platform strategy, designing trusted datasets, defining best practices, and helping shape the future of analytics at MNTN.</p> <p><strong>What you'll do</strong></p> <ul> <li>Lead the design and evolution of scalable, trusted dimensional models including well-defined facts and conformed dimensions that power reporting, product analytics, forecasting, and decision-making across the business.</li> <li>Treat analytics code as software: establish standards for modular, DRY transformations, automated testing, version control, CI/CD, documentation, and governance so data quality and consistency are enforced by the system rather than by manual effort.</li> <li>Replace brittle, high-maintenance pipelines with reusable, automated patterns (incremental models, declarative transformations, reusable macros) that reduce toil and keep the platform maintainable at scale.</li> <li>Partner with engineering to shape data architecture, improve platform performance, and support long-term scalability.</li> <li>Collaborate with stakeholders across Product, Finance, Revenue, and Operations to translate business needs into durable analytical solutions.</li> <li>Drive adoption of semantic layers, shared metrics, and self-service analytics capabilities that increase trust and accessibility across teams.</li> <li>Provide technical leadership through architectural guidance, mentorship, and influence across the broader data organization.</li> </ul> <p><strong>What success looks like</strong></p> <ul> <li>Trusted, well-documented dimensional models and metric definitions exist across key business domains, reducing reporting inconsistencies and increasing confidence in decision-making.</li> <li>Analytics engineering standards for modeling, automated testing, governance, and documentation are established and broadly adopted across the data organization.</li> <li>The pipeline runs reliably with minimal manual intervention; new data needs are met by extending well-structured models rather than building yet another bespoke table.</li> <li>Stakeholders across Product, Finance, Revenue, and Operations can access reliable self-service data resources without requiring constant support from technical teams.</li> <li>The analytics platform is more scalable, performant, and maintainable because of systems, processes, and architectural improvements you've led.</li> </ul> <p><strong>What you'll bring</strong></p> <ul> <li>8+ years of experience in analytics engineering, business intelligence, data engineering, or a related data discipline.</li> <li>Deep, hands-on expertise in <strong>Kimball-style dimensional modeling</strong> including fact and dimension table design, grain definition, surrogate keys, slowly changing dimensions, conformed dimensions, and star (vs. snowflake) schema tradeoffs plus the judgment to know when to apply or break these patterns.</li> <li>Expert SQL and strong command of <strong>dbt or SQLMesh</strong>, with a demonstrated habit of building modular, tested, version-controlled transformations rather than manually maintained scripts.</li> <li>Production experience with modern cloud data warehouses such as Snowflake, Databricks, BigQuery, or Redshift.</li> <li>A track record of building automated, low-toil analytical systems including incremental processing, CI/CD, data tests/contracts, that stay maintainable as data and team size grow.</li> <li>Proven ability to influence cross-functional stakeholders and drive alignment on metrics, reporting, and data strategy.</li> <li>Experience leading large-scale analytics initiatives with company-wide impact, operating as a senior technical leader without direct authority.</li> <li>Strong communication skills with the ability to translate complex technical concepts into clear business outcomes.</li> </ul> <p><strong>Bonus points if you have</strong></p> <ul> <li>Experience supporting product analytics, experimentation, or marketplace platforms.</li> <li>Familiarity with modern BI and semantic layer tools such as Looker, Sigma, Hex, or Tableau.</li> <li>Experience in adtech, martech, SaaS, or other high-scale data environments.</li> <li>Exposure to machine learning, AI applications, or advanced analytics workflows.</li> <li>Experience helping organizations mature data governance and metric standardization practices.</li> </ul><div class="content-conclusion"><p><strong>MNTN Perks</strong></p> <ul> <li>100% remote within the US</li> <li data-stringify-indent="0" data-stringify-border="0">Flexible vacation policy</li> <li data-stringify-indent="0" data-stringify-border="0">Annual vacation allowance for travel related expenses</li> <li>Three-day weekend every month of the year</li> <li>Competitive compensation</li> <li>100% healthcare coverage</li> <li>401k plan</li> <li>Flexible Spending Account (FSA) for dependent, medical, and dental care</li> <li>Access to coaching, therapy, and professional development</li> </ul> <p><strong>About MNTN</strong></p> <p>Our recruiters will always reach out using an email address ending with @mountain.com OR @mntn.com. If you’re contacted by someone without that address and they mention a Reference Code (which we never use), then&nbsp;<em data-stringify-type="italic">that ain’t us folks.&nbsp;</em>Tell those trolls to take a hike–you’re waiting to climb a MNTN.</p> <p>MNTN provides advertising software for brands to reach their audience across Connected TV, web, and mobile. MNTN Performance TV has redefined what it means to advertise on television, transforming Connected TV into a direct-response, performance marketing channel. Our web retargeting has been leveraged by thousands of top brands for over a decade, driving billions of dollars in revenue.</p> <p>Our solutions give advertisers total transparency and complete control over their campaigns all with the fastest go-live in the industry. As a result, thousands of top brands have partnered with MNTN, including Tarte, Decked, and National University.</p> <p>#LI-Remote</p></div>