Senior Data Analyst (Contract)

Tech HoldingLos Angeles, CA
5hHybrid

About The Position

We are seeking a Senior Data Analyst, for a project based assignment, to deliver high-quality analytics and insights that deepen the organization’s understanding of clients, collections, and commercial performance across boutiques, ecommerce, and wholesale channels. This is a hybrid role combining approximately 60% data analytics and 40% analytics-focused data engineering. You will build analytics-ready datasets and semantic models while leading deep commercial and client analysis, performance storytelling, and structured test-and-learn measurement. Reporting to the Manager, Data & Commercial Analytics, this role serves as a critical bridge between raw enterprise data and business decision-making, ensuring leaders operate with trusted metrics, reliable datasets, and actionable insights.

Requirements

  • 5–8 years of experience in analytics or analytics engineering roles.
  • Advanced SQL proficiency.
  • Experience working with modern data warehouse environments.
  • Experience working in AWS or GCP environments.
  • Experience with BI tooling and retail KPI frameworks.
  • Strong commercial acumen and performance storytelling skills
  • High discretion and sound judgment.
  • Polished and concise communication.
  • Comfortable balancing analytical rigor with creative nuance.
  • Pragmatic and accountable.
  • Strong ability to translate data into commercial decisions.

Nice To Haves

  • Experience with dbt or similar analytics engineering tools.
  • Exposure to Python or R.
  • Experience in luxury or premium retail environments.

Responsibilities

  • Lead client lifecycle, segmentation, and performance analysis.
  • Deliver assortment, sell-through, margin, and boutique-level insights.
  • Provide executive-ready narratives and actionable recommendations.
  • Support structured test-and-learn measurement initiatives.
  • Build and maintain analytics-ready datasets and data marts.
  • Maintain semantic models and KPI definitions.
  • Develop SQL transformations and implement data quality checks.
  • Improve data consistency, performance, and reliability.
  • Develop automated dashboards and KPI reporting frameworks.
  • Support campaign, capsule, and clientele performance measurement.
  • Translate analytical findings into clear business action.
  • Mentor analysts and data power users.
  • Act as a trusted analytics partner across commercial teams.
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