- Company Name
- Lawhive
- Job Title
- Data Analyst (Product)
- Job Description
-
**Job Title:**
Data Analyst (Product)
**Role Summary:**
Own and scale product analytics by building data models, designing experiments, and delivering actionable insights. Partner with Product, Engineering, Design and AI teams to define success metrics, track user behavior, and quantify feature impact. Provide real‑time dashboards, establish a single source of truth, and champion a culture of experimentation that drives product improvement.
**Expectations:**
- Deliver rigorous, statistically sound A/B tests and experiments.
- Translate analytical findings into clear product recommendations.
- Own end‑to‑end analytics pipeline from data ingestion to stakeholder reporting.
- Work autonomously within a small, high‑impact data team.
**Key Responsibilities:**
1. Build and maintain core data models in dbt; ensure alignment with product use cases.
2. Design, run, and analyze controlled experiments (A/B, multivariate, Bayesian, etc.) to evaluate feature changes.
3. Define and track product KPIs, create dynamic dashboards (Hex, PostHog, custom BI) for real‑time visibility.
4. Partner with Engineering to validate event tracking, instrument new launches, and maintain data quality.
5. Analyze user journeys to identify adoption patterns, friction points, churn drivers, and retention levers.
6. Quantify feature opportunity sizing and inform roadmap priorities.
7. Work with AI team on evaluation metrics for LLM–based tools; design experiments for prompt/model variants.
8. Develop tagging strategies via Google Tag Manager and PostHog; collaborate on data architecture decisions.
**Required Skills:**
- 3–5+ years in product analytics, data analytics, or data science roles.
- Advanced SQL (BigQuery, Snowflake, etc.) for complex queries and data transformation.
- Proficient with dbt, dbt models, and modern data stacks.
- Strong foundation in A/B testing methodology, statistical significance, hypothesis testing, and experiment design.
- Experience with analytics tools: PostHog, Segment, Google Tag Manager, Hex, Tableau, Looker, or similar.
- Ability to translate data insights into actionable product recommendations.
- Excellent written and verbal communication; ability to distill complex analysis for cross‑functional stakeholders.
- Collaborative mindset; willing to own analytics ownership and champion experimentation culture.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field.
- Preferable additional certifications in SQL, data analysis or analytics platforms (e.g., Google Data Analytics Professional Certificate, dbt certification) are advantageous but not mandatory.