Analytics Engineer

Role Mission

Turn raw/operational data into
analysis-ready models
and a
trusted metrics layer
that enables consistent BI, experimentation, and decision-making.

Scope Boundaries

• 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

1. Trusted metrics layer
   implemented for top business domains، 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.

Core Responsibilities

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

A-Player Competencies

• 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).