Temporal Technologies - Senior Analytics Engineer

• Build and maintain reusable analytical data products across Temporal's business domains. • Define canonical entities, shared dimensions, purpose-built marts, metric components, explicit grains, keys, lineage, and history behavior. • 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. • Reduce duplicated or conflicting logic across source tables, marts, Omni workbooks, dashboards, notebooks, and analytical agents. • 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. • Make important data products understandable and usable for people and analytical agents through documented contracts, metadata, ownership, quality status, and retrieval guidance. • Contribute to architecture decisions, design reviews, code reviews, and coaching while remaining hands on in SQL/code, modeling, testing, and documentation. • What You’ll Focus on First • During your first several months, you will help the team: • Document ownership of the core Analytics Products portfolio and identify high-risk duplicated or conflicting metric logic. • 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.