{"id":2199673,"url":"https://alion.io/job/temporal-senior-analytics-engineer","title":"Senior Analytics Engineer","company":{"id":4236,"name":"Temporal","domain":"temporal.io","url":"https://alion.io/company/temporal","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":12,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-14T06:34:16Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":137600,"max":220000,"currency":"USD","period":"year","gross":null,"usd_annual":220000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"CI/CD","optional":false},{"name":"Git","optional":false},{"name":"Looker","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-09T18:18:37Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-15T00:22:59Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About the Role\nThe Senior Analytics Engineer will help build the reusable analytical models, tools, and systems to power decisions across Temporal. This role reports to the Director of Data Engineering and Analytics. Ultimately, your goal is to make it possible for every Temporal employee to produce first-class analyses.\nYou will work across multiple business domains, including Product, GTM, Finance, and Engineering to deeply understand their needs and build solutions that enable both people and agents to reliably answer their analytical questions. You will architect and implement the data layer that sits between our landed source data and our various user interfaces via canonical entities, shared dimensions, metric definitions, data marts, etc. That includes making core concepts such as accounts, namespaces, activation, billable usage, and revenue consistent wherever they are used.\nYou will take direct ownership of work that is currently distributed across the Data Engineering and Analytics team, including data modeling, data mart and Omni Topic creation, and enablement. Along with helping to align our business logic across marts and dashboards, you will create clear technical governance and controls as you work to empower stakeholders across the business.\nYou will work closely with Data Platform and Trust on orchestration, deployment, observability, access controls, performance, reliability, and cost. You will partner with Data Science and Applied AI on feature tables, model outputs, model consumption, and monitoring. You will work with business functions on source system hygiene, prioritization of tasks, and validating business meaning of shared metrics.\nThe ideal candidate possesses strong SQL skills, sound business acumen, and deep expertise in building semantic products gained through years of experience across different tools, modeling frameworks, and subject areas. The candidate is comfortable with ambiguous business questions, knows when to build a reusable data product vs modify an existing one, and how to deliver practical solutions in keeping with an overall governance framework and strategy. You will be hands-on writing code, tests, documentation, and performing technical reviews.\nWhat You’ll Do\nBuild and maintain reusable analytical data products across Temporal's business domains.\n\nDefine canonical entities, shared dimensions, purpose-built marts, metric components, explicit grains, keys, lineage, and history behavior.\n\nDesign and maintain core parts of the Omni semantic layer and certified Topics, with clear definitions for measures, dimensions, joins, and time handling.\n\nEstablish and improve standards for modeling, metric implementation, documentation, and change management. Build automated checks for freshness, completeness, uniqueness, and relationships between datasets, and make failures visible to the teams that own them.\n\nReduce duplicated or conflicting logic across source tables, marts, Omni workbooks, dashboards, notebooks, and analytical agents.\n\nPartner with Data Platform and Trust on deployment, orchestration, observability, access controls, performance, cost, and production reliability.\n\nPartner with Data Science and Applied AI on feature tables, prediction outputs, model consumption, and monitoring.\n\nWork with domain partners and senior stakeholders to turn ambiguous questions into precise definitions, reusable models, and trusted ways for teams to answer follow-up questions themselves.\n\nHelp qualified contributors outside Data Engineering and Analytics create tables, models, notebooks, and workbooks through controlled sandboxes, templates, automated checks, review, and promotion into shared-use environments.\n\nMake important data products understandable and usable for people and analytical agents through documented contracts, metadata, ownership, quality status, and retrieval guidance.\n\nContribute to architecture decisions, design reviews, code reviews, and coaching while remaining hands on in SQL/code, modeling, testing, and documentation.\n\nWhat You’ll Focus on First\nDuring your first several months, you will help the team:\n\nDocument ownership of the core Analytics Products portfolio and identify high-risk duplicated or conflicting metric logic.\n\nPublish the initial canonical entity and mart architecture, including clear standards for grain, keys, history, lineage, and quality controls.\n\nLaunch governed contribution templates, automated checks, and a review and promotion process for qualified contributors outside DEA.\n\nCreate a practical certification and retirement process for metrics, Topics, dashboards, and shared data products.\n\nDeliver an initial version of one agreed priority data product, such as a revenue, PLG, or account-level model.\n\nWhat You’ll Bring\nAdvanced SQL experience, including the ability to reason about correctness, performance, and maintainability.\n\nWorking proficiency in Python to build and maintain jobs that create and alter tables in the data lake.\n\nExperience with data transformation frameworks such as DBT, SQLMesh, etc\n\nData mart design experience using modeling techniques such as dimensional and entity centric modeling.\n\nExperience implementing and maintaining a semantic layer or governed metrics.\n\nAdvanced knowledge of grain, keys, history, lineage, contracts, and quality controls.\n\nGit, review, automated tests, CI/CD, and production ownership.\n\nTranslating ambiguous questions into reusable products.\n\nImproving self-service while maintaining shared definitions.\n\nWorking with senior business and technical stakeholders.\n\nHelpful Experience\nS3, Athena, Iceberg, Hive, or another open lakehouse architecture.\n\nHands-on experience with Omni Analytics, Looker, or a comparable semantic layer.\n\nExperience orchestration systems.\n\nUsage-based B2B SaaS, cloud infrastructure, developer tools, or consumption-based revenue.\n\nBuilding governed contribution programs.\n\nDesigning metadata or semantic products for analytics agents.\n\nTemporal Technologies is an Equal Opportunity Employer. Temporal Technologies does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need. We embrace and celebrate differences and diversity.\nTemporal is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. 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