{"id":1915151,"url":"https://alion.io/job/tubescience-senior-analytics-engineer","title":"(Senior) Analytics Engineer","company":{"id":686685,"name":"TubeScience","domain":"tubescience.com","url":"https://alion.io/company/tubescience","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":84,"open_postings":11,"ghost_share":0,"stale_share":0.545,"repost_share":0.182,"time_to_fill_p50_days":42,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Los Angeles, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":110000,"max":165000,"currency":"USD","period":"year","gross":null,"usd_annual":165000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"Machine Learning","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Databricks","optional":true}],"status":"live","first_seen_at":"2026-10-05T14:35:59Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-11-06T22:36:33Z","board_verified":true,"closed_at":null,"days_open":32,"trust":{"level":"ok","repost_count":2,"flags":["repost"],"days_open":31},"description":"(Senior) Analytics Engineer\nTubeScience Labs: Los Angeles, in person - $110,000-$165,000\nTubeScience Labs' mission is to create trusted, scalable, and self-improving AI systems that power the largest performance-based paid social creative video company in the world.\nTubeScience is Meta's largest creative partner and AppLovin's #1 creative partner, producing 8,000+ original ads every month from a 100,000 sq. ft. Los Angeles studio, backed by a library of 1.6 million+ performance ads and $3B in annual managed ad spend. That is what gives TubeScience one of the richest first-party creative-performance datasets anywhere.\nLabs turns that data - and the playbook behind billions in spend - into frontier AI tools that actually ship. We are an AI-native lab working end to end, from research and design to coding, experimentation and delivery. Our pipelines run against real creative, real deadlines, and real budgets every day.\nYou'll join a small, AI-empowered, senior team of engineers and product managers, with direct access to expert users and full ownership of the systems you build. You'll be expected to use the latest frontier and open-weight models in every phase of the job, from discovery and prototyping to building, testing and deployment.\nThe role\nYou'll build the data systems that connect ad performance to creative decisions. Our platform brings together data from advertising channels, creative production workflows and business operations, and your work makes that data reliable, understandable and useful in reports, internal products, and future analytics and machine learning applications.\nThe role is hands-on across the whole data lifecycle: extracting data from APIs, maintaining dependable pipelines, modeling data in Snowflake, defining metrics, and delivering insights through Power BI and other tools. Much of the foundation already exists. You'll learn how its parts fit together, make it faster and more reliable, and help shape what we build next.\nWe weigh directly relevant experience heavily. This platform carries real advertising data and feeds the reporting the business runs on from the first week, so tell us plainly where your background maps: advertising data, warehouse modeling, pipelines or BI.\nWe are open to hiring at Analytics Engineer or Senior Analytics Engineer level, depending on experience and scope of ownership.\nResponsibilities\nBuild and evolve the data platform, from external sources through ingestion, transformation and curated models to BI and application delivery\nExtract and integrate advertising data from Meta, TikTok, Google and Snapchat using their APIs and Python, handling pagination, rate limits, backfills, schema changes and reconciliation with the platforms\nBuild performant Snowflake models, and define and document the metrics the business relies on together with stakeholders\nMaintain and improve Power BI reporting, including a possible migration to a better BI tool, and build datasets that link ad outcomes to creative strategy and production decisions\nInvestigate discrepancies and pipeline failures, add checks and observability, and use agents to automate the repetitive work\nWork with data, product, engineering and business partners on analytics and machine learning approaches that feed ad performance back into creative development\nMinimum qualifications\nYou have 5+ years in analytics engineering, data engineering or a closely related role, with meaningful ownership of production data systems.\nYou have worked with advertising or marketing performance data, ideally pulling it directly from platforms such as Meta, TikTok, Google or Snapchat.\nYou are strong in SQL and Python, and you can reason through the full path from a source API to a business-facing metric or report.\nYou have designed data models and warehouse architecture that support changing business needs, including layered approaches such as medallion architecture where useful.\nYou have built or maintained orchestrated pipelines, and you understand failure recovery, backfills, data quality and operational reliability.\nYou have developed Power BI reports or semantic datasets, and you have worked with stakeholders to resolve ambiguous metric definitions.\nYou are comfortable with GitHub-based development, code review, and cloud infrastructure on AWS or GCP.\nYou investigate problems independently, make practical engineering decisions, and explain the trade-offs to technical and business partners alike.\nPreferred qualifications\nYou have worked on creative analytics, attribution or cross-platform performance measurement.\nYou have optimized Snowflake or Databricks performance, including dynamic tables.\nYou have used transformation tools such as Coalesce and orchestration tools such as DBOS or Airflow.\nYou have built datasets or features for internal applications, experimentation or machine learning.\nYou use AI-assisted development fluently, while applying your own judgment to system design, correctness and production changes.\nThe problems to solve\nAdvertising data from many platforms. Meta, TikTok, Google and Snapchat, each with its own API, limits and quirks, reconciled with what the platforms themselves report.\nA platform that grows with the business. Architectural choices that still hold as data volumes and use cases grow.\nMetrics people trust. Consistent definitions across advertising, creative, client and operational data, agreed with stakeholders and documented.\nReporting that connects ads to creative. Datasets that show which creative decisions actually moved performance.\nReliability without the toil. Fewer surprises in the pipelines, with agents handling the repetitive checks.\n Feedback loops. Analytics and machine learning that feed ad performance back into creative development.\nHow the hiring works\nThree conversations and a short piece of practical work. Recruiter screen, then an engineer from the team, then a practical assignment and a walkthrough with the hiring manager. Roughly 17 business days end to end if we both move quickly. 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