# Senior Analytics Engineer

**Company**: Asana
**Location**: Vancouver, BC
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: $106,000 - $120,000 CAD annually
**Category**: Data Science
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/asana/jobs/8282814?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_aef74192-fd4

## Description

The Data Science & Analytics team at Asana is how the company turns data into decisions , defining the questions that matter, surfacing the answers, and making sure insight is at the center of every critical product and business call.

As a Senior Analytical Engineer, you sit at the intersection of Data Engineering, Analytics, and Data Science, and you own the data foundations for a business domain end to end. Your mandate is to turn raw data into reliable, business-ready datasets that PMs, analysts, data scientists, and leaders actually trust and use , and to define the business logic and metric standards that make AI-powered self-serve trustworthy.

**What you’ll achieve**

- Own the Gold layer for a given business domain (e.g., PLG funnel, marketing attribution, revenue, NPI/AWM): Design and continuously improve the curated, dimensional data models that downstream dashboards, Genie spaces, and ELT reporting depend on.

- Implement the canonical business logic behind your domain's core KPIs: Translate KPIs into governed, versioned metric marts that resolve "this number doesn't match" disputes for good.

- Build and curate the semantic layer and Genie spaces that power self-serve in your domain: Author the metadata, documentation, and prompt/metric definitions that let stakeholders query governed data in plain language through Claude and Databricks Genie.

- Own the metric dictionary for your domain: a single source of truth for what each metric means, who owns it, and where to find it. Partner with peers across DS&A to keep KPI definitions consistent where domains overlap.

- Author data contracts and SLAs at the Silver→Gold boundary, partnering with Horizontal Data Engineering on the inputs you depend on, and owning data quality, freshness, and oncall for Gold/metric-mart failures in your domain.

- Build and maintain certified, board-ready dashboards on governed Gold data, partnering with Data Science to translate insight requirements into trusted, reusable products rather than one-off builds.

- Partner directly with Product & Business, Data Science, and Engineering to turn ambiguous, underspecified questions into scalable datasets , anticipating downstream reporting impacts before they become incidents, and raising the data-model quality bar across the domains you touch.

**About you**

- Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.

- 4+ years in analytics engineering, data engineering, or a closely related analytics role, with a track record of independently owning the data models a team relies on for decisions.

- Advanced SQL and strong data modeling fundamentals: dimensional modeling, star/snowflake schemas, slowly changing dimensions, and semantic layer design.

- Hands-on experience with a transformation framework (dbt or equivalent), orchestration tooling (e.g. Airflow), version control (Git), and modern warehouse/lakehouse platforms (Databricks experience preferred).

- Practical experience with data quality testing and observability, schema management and data contracts, and query/model performance and cost tuning.

- Demonstrated domain fluency in at least one business area (e.g. PLG funnels, SLG pipeline, marketing attribution, Product telemetry, revenue/ARR) and the judgment to translate "I don't trust this number" into a specific, durable model fix.

- Strong cross-functional partnership skills: requirement gathering, prioritization, documentation and enablement, driving alignment on metric definitions, and explaining technical tradeoffs to non-technical partners.

- Curiosity about AI-native analytics , NL2SQL, metadata/semantic layers for self-serve, and using tools like Claude and Genie to multiply your reach rather than replace rigor. Exposure to Unity Catalog, Looker/LookML, or reverse-ETL/activation (Salesforce, Marketo, Gainsight) is a plus

**What we’ll offer**

- Generous, transparent and fair compensation system (base salary and RSUs)

- Health insurance with dental

- Breakfast and lunch catering on the days that you work from the office

- Home office setup budget

- Gym/Fitness card

- Fertility healthcare and family-forming support with Carrot

- Mental Health Support in Modern Health

- Group life insurance

- MacBooks with all necessary accessories

## Skills

### Required
- SQL
- data modeling
- dimensional modeling
- star/snowflake schemas
- slowly changing dimensions
- semantic layer design
- dbt
- Airflow
- Git
- Databricks

### Nice to have
- Unity Catalog
- Looker/LookML
- reverse-ETL/activation (Salesforce, Marketo, Gainsight)

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/asana/jobs/8282814?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)