Senior Analytics Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and sustain reusable analytical data products across various business sectors of Temporal.

• Define canonical entities, shared dimensions, purpose-built marts, metric components, explicit grains, keys, lineage, and historical behavior.

• Design and oversee the Omni semantic layer and certified Topics.

• Set standards for modeling, metric implementation, documentation, and change management.

• Create automated checks for freshness, completeness, uniqueness, and dataset relationships.

• Minimize duplicated or conflicting logic across source tables, marts, workbooks, dashboards, notebooks, and analytical agents.

• Collaborate with Data Platform and Trust on deployment, orchestration, observability, access controls, performance, cost, and reliability.

• Work with Data Science and Applied AI on feature tables, prediction outputs, model consumption, and monitoring.

• Convert ambiguous stakeholder inquiries into clear definitions, reusable models, and self-service analytical solutions.

• Empower qualified contributors to develop tables, models, notebooks, and workbooks through regulated processes.

• Document contracts, metadata, ownership, quality status, and retrieval guidance for individuals and analytical agents.

• Participate in architecture, design reviews, code reviews, coaching, SQL/code, modeling, testing, and documentation.

• Record analytics product ownership and identify duplicated or conflicting metric logic.

• Publish standards for canonical entity and mart architecture.

• Initiate governed contribution templates, automated checks, reviews, and promotion processes.

• Establish certification and retirement processes for metrics, Topics, dashboards, and shared data products.

• Deliver an initial priority data product, such as a revenue, PLG, or account-level model.

• Report to the Director of Data Engineering and Analytics.

⛳️ Requirements

• Advanced SQL proficiency, focusing on correctness, performance, and maintainability.

• Working knowledge of Python for the development and maintenance of data-lake table jobs.

• Experience with data transformation frameworks like DBT or SQLMesh.

• Data mart design utilizing dimensional and entity-centric modeling.

• Experience in implementing and maintaining a semantic layer or governed metrics.

• In-depth understanding of grain, keys, history, lineage, contracts, and quality controls.

• Familiarity with Git, code reviews, automated testing, CI/CD, and production ownership.

• Ability to transform ambiguous queries into reusable products.

• Experience in enhancing self-service while ensuring shared definitions are maintained.

• Proven track record of collaborating with senior business and technical stakeholders.

• Helpful: Experience with S3, Athena, Iceberg, Hive, or other open lakehouse architectures.

• Helpful: Familiarity with Omni Analytics, Looker, or similar semantic layers.

• Helpful: Experience with orchestration systems.

• Helpful: Background in usage-based B2B SaaS, cloud infrastructure, developer tools, or consumption-based revenue.

• Helpful: Experience in building governed contribution programs.

• Helpful: Expertise in designing metadata or semantic products for analytical agents.

🏝️ Benefits

• Equity.

• Remote work arrangement.

• Reasonable accommodation for individuals with disabilities.

• AI interview recording/transcription with the option to opt out without affecting candidacy.