[Hiring] Senior Analytics Engineer @Temporal Technologies

Role Description

The 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.

You 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.

You 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
• 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.

You will work closely with Data Platform and Trust on:

• Orchestration
• Deployment
• Observability
• Access controls
• Performance
• Reliability
• Cost

You will partner with Data Science and Applied AI on:

• Feature tables
• Model outputs
• Model consumption
• Monitoring

You will work with business functions on:

• Source system hygiene
• Prioritization of tasks
• Validating business meaning of shared metrics

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. 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.

Qualifications

• Advanced SQL experience, including the ability to reason about correctness, performance, and maintainability.
• 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.
• 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.

Requirements

• 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.

Benefits

• 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.