Analytical Engineer

Asana Asana · Enterprise · Warsaw, Poland · Data

This role focuses on building and maintaining the 'Gold' and semantic layers of data, ensuring reliability and trustworthiness for business intelligence and self-serve analytics. It involves implementing business logic, defining metrics, and creating data models that power dashboards and AI-assisted querying tools like Claude and Databricks Genie. The role requires strong data modeling, SQL, and experience with data transformation frameworks.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 4+ years in analytics engineering, data engineering, or a closely related analytics role
  • Advanced SQL
  • 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)
  • Hands-on experience with orchestration tooling (e.g. Airflow)
  • Hands-on experience with version control (Git)
  • Hands-on experience with modern warehouse/lakehouse platforms (Databricks experience preferred)
  • Practical experience with data quality testing
  • Practical experience with observability
  • Practical experience with schema management
  • Practical experience with data contracts
  • Practical experience with 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)
  • Strong cross-functional partnership skills

Nice to have

  • Databricks experience
  • 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
  • Exposure to Looker/LookML
  • Exposure to reverse-ETL/activation (Salesforce, Marketo, Gainsight)

What the JD emphasized

  • Own the data foundations for a business domain end to end
  • turn raw data into reliable, business-ready datasets that PMs, analysts, data scientists, and leaders actually trust and use
  • define the business logic and metric standards that make AI-powered self-serve trustworthy
  • track record of independently owning the data models a team relies on for decisions
  • Advanced SQL and strong data modeling fundamentals
  • 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
  • Strong cross-functional partnership skills