الرجوع

Analytics Engineer (Data Analytics Engineer)

Jeddah

Scope boundaries (what you own)

  • Owns: dbt-style transformations, dimensional/semantic modeling, metric definitions, data testing, analytics-ready layers, documentation.
  • Does not own (but partners): raw ingestion, infra cost optimization, BI pixel-perfect delivery (BI Dev owns), data governance policy (Governance owns).

12-month success outcomes (scorecard)

  1. Trusted metrics layer implemented for top business domains (e.g., Revenue, Customer, Product, Ops).
    1. Metric drift incidents reduced by ≥80%.
  2. Model reliability: ≥ 99% of analytics models meet freshness + completeness expectations.
  3. Time-to-insight: reduce time to produce a new KPI/dataset from request → production by 50%.
  4. Adoption: ≥ 70% of recurring reporting uses certified semantic models/metrics (not ad-hoc SQL).

 

90-day deliverables

  • Stand up (or harden) the analytics modeling standards: naming, grain, SCD strategy, tests, documentation.
  • Deliver one end-to-end domain model (bronze/silver/gold or raw/stage/mart) + certified KPIs.
  • Implement data quality tests (schema, uniqueness, accepted values, referential integrity) and alerting for that domain.

طلب وظيفة



Core responsibilities (ongoing)

  • Build and maintain transformation pipelines (ELT/ETL) from raw → curated marts.
  • Design dimensional models: facts, dimensions, conformed dimensions, SCD patterns.
  • Define and govern metric logic in a semantic layer (dbt Semantic Layer / LookML / SSAS tabular equivalent).
  • Partner with BI Devs to ensure dashboards map to certified metrics (no “shadow KPIs”).
  • Own analytics data documentation (catalog descriptions, lineage notes, usage guidance).
  • Conduct model performance tuning (partitioning, clustering, incremental strategies).

Key interfaces

  • Data Engineering: upstream contracts, schema changes, CDC semantics.
  • BI: dashboard requirements, KPI definitions, visualization constraints.
  • Data PM: roadmap prioritization, adoption goals.
  • Governance: definitions, stewardship, certification process.

KPIs (leading + lagging)

  • Leading: % models with tests, docs completeness, build time, review cycle time.
  • Lagging: metric discrepancy incidents, dashboard trust score, adoption of certified models.

A-player competencies (Topgrading-style)

  • Systems thinking: understands upstream/downstream ripple effects.
  • Semantic rigor: defines grain, metric logic, edge cases explicitly.
  • Pragmatic standards: enforces consistency without blocking delivery.
  • Stakeholder translation: converts business questions into data contracts and models.
  • AY behaviors: Trust at Scale, Clarity Over Complexity, Ownership.

Minimum qualifications

  • Proven analytics modeling in a warehouse/lakehouse (Snowflake/BigQuery/Redshift/Synapse/Databricks).
  • Strong SQL + modeling patterns (Kimball, data vault exposure acceptable).
  • Experience with dbt or equivalent transformation framework.
  • Familiarity with BI tools’ semantic behaviors (Power BI DAX, Tableau LODs, Looker).

Work sample (recommended)

  • Given 4 raw tables + messy requirements, produce:
    • 1 fact, 3 dims, 5 KPIs,
    • tests + documentation,
    • and explain grain + edge cases.