Temporal Technologies

Temporal Technologies

Senior Analytics Engineer

United States (Remote) · Senior

Sponsorship not specifiedDetected 13 days ago
PythonCode ReviewGitSQLCloud PlatformsCI/CDTemporaldbtData EngineeringData ScienceData VisualizationMentoring

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Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Files H-1B transfers

11 transfer filings in the last year, covering 11 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

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About the role

  • 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.
  • That includes making core concepts such as accounts, namespaces, activation, billable usage, and revenue consistent wherever they are used.

Responsibilities

  • Build and maintain reusable analytical data products across Temporal's business domains.
  • Design and maintain core parts of the Omni semantic layer and certified Topics, with clear definitions for measures, dimensions, joins, and time handling.
  • Establish 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.
  • Partner with Data Platform and Trust on deployment, orchestration, observability, access controls, performance, cost, and production reliability.
  • Partner with Data Science and Applied AI on feature tables, prediction outputs, model consumption, and monitoring.
  • Work 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.
  • Help 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.
  • Contribute to architecture decisions, design reviews, code reviews, and coaching while remaining hands on in SQL/code, modeling, testing, and documentation.
  • Document ownership of the core Analytics Products portfolio and identify high-risk duplicated or conflicting metric logic.

Requirements

  • Working proficiency in Python to build and maintain jobs that create and alter tables in the data lake.
  • Experience with data transformation frameworks such as DBT, SQLMesh, etc
  • The 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.

Company info

  • Publish the initial canonical entity and mart architecture, including clear standards for grain, keys, history, lineage, and quality controls.
  • Launch governed contribution templates, automated checks, and a review and promotion process for qualified contributors outside DEA.
  • Create a practical certification and retirement process for metrics, Topics, dashboards, and shared data products.
  • Deliver an initial version of one agreed priority data product, such as a revenue, PLG, or account-level model.
  • Advanced SQL experience, including the ability to reason about correctness, performance, and maintainability.
  • Data mart design experience using modeling techniques such as dimensional and entity centric modeling.
  • Experience implementing and maintaining a semantic layer or governed metrics.
  • Advanced knowledge of grain, keys, history, lineage, contracts, and quality controls.
  • Git, review, automated tests, CI/CD, and production ownership.
  • Translating ambiguous questions into reusable products.
  • Improving self-service while maintaining shared definitions.
  • Working with senior business and technical stakeholders.
  • S3, Athena, Iceberg, Hive, or another open lakehouse architecture.
  • Hands-on experience with Omni Analytics, Looker, or a comparable semantic layer.
  • Experience orchestration systems.
  • Usage-based B2B SaaS, cloud infrastructure, developer tools, or consumption-based revenue.
  • Building governed contribution programs.
  • Designing metadata or semantic products for analytics agents.
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This listing is sourced directly from Temporal Technologies's careers page and normalized into a canonical job model.