Senior Analytics Engineer

Overview

Build and own the reusable analytical data layer (canonical entities, shared dimensions, metric definitions, data marts and Omni Topics) that lets teams across Temporal answer analytical questions consistently.

What you'll do

• 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 need

• 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.

Nice to have

• S3, Athena, Iceberg, Hive, or another open lakehouse architecture.
• Hands-on experience with Omni Analytics, Looker, or a comparable semantic layer.
• Experience with 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.

Details

• Location: United States (Remote).
• Reports to the Director of Data Engineering and Analytics.