شرح موقعیت
Senior Analytics Engineer We're building our data warehouse from scratch, and we need someone who can own it end to end — pull raw data out of our source systems into the bronze layer, model it into clean and trusted datasets, and then use it to answer the questions the business is actually asking. This role sits between Data Engineering, BI, and Analytics. Half of it is building. The other half is analysis: being the person product, operations, marketing, and finance come to — and who can say not just what the number is, but why it moved and whether it's right. Airflow (or Dagster / Prefect / n8n) · dbt · medallion architecture (bronze–silver–gold) · CDC and incremental extraction · ClickHouse, BigQuery, or Snowflake · Git · product or growth analytics · marketplace experience Our stack PostgreSQL · Python · Airflow · Superset · Git · Docker The warehouse doesn't exist yet — the architecture decisions are still open, and you'll have a real voice in them.
مسئولیتها
- Build ingestion pipelines from production Postgres, APIs, and third-party tools into the warehouse's bronze layer, orchestrated in Airflow.
- Model raw data into reusable, documented analytical datasets using dimensional modeling — and set the standards for naming, metric definitions, and ownership.
- Own day-to-day analytics for product, ops, marketing, finance, and leadership: funnels, user behavior, feature and initiative performance, growth opportunities and bottlenecks.
- Design and maintain KPIs and dashboards in Superset, and push toward self-service.
- Validate data accuracy, reconcile discrepancies across sources, and root-cause why two numbers disagree.
- Automate manual reporting work with Python and APIs — including automated alerts and insights.
- Document data models, reporting logic, and metric definitions.
- Identify where LLMs and AI agents create real leverage, and prototype and evaluate those solutions.
نیازمندیها
- 3+ years as an Analytics Engineer, BI Developer, Data Analyst, Data Engineer, or similar.
- Advanced SQL — complex queries, window functions, and the judgment to know when a query is slow because of the query and when it's slow because of the model.
- Hands-on PostgreSQL, including indexes and execution plans.
- Strong data modeling for analytics and reporting.
- Python for analysis, automation, and data processing.
- Experience building dashboards and BI reporting (Superset, Metabase, Power BI, Tableau — the tool matters less than the thinking).
- Experience with data validation and reconciliation, and the instinct to distrust a number that looks too good.
- Ability to translate business needs into analytical logic and explain results to non-technical stakeholders.
- Practical familiarity with LLMs and AI agents.