Вакансия Senior Data Analyst – Product Analytics

Skills

• SQL
• Python
• DataFrames
• Pandas
• PyArrow
• Jupyter Notebook
• RegExr
• A/B testing
• Superset
• Microsoft Power BI
• MicroStrategy
• Amazon QuickSight
• Looker
• Tableau
• Apache Airflow
• Git
• dbt
• Redshift
• Snowflake
• BigQuery
• Amazon S3
• Google Analytics
• AWS

Описание вакансии

We are looking for an experienced and collaborative Senior Data Analyst to join our Product Analytics team! In this role, you will be pivotal in shaping our product development through data-driven insights, partnering with product teams to support informed, proactive decision-making.

Key Responsibilities:

• Data-Driven Marketplace: Act as a key analytical partner to product teams, driving data-centric product development through robust analysis and actionable insights.
• Customer Understanding: Leverage data to deeply understand user behavior, such as search habits, and drive business value by improving product taxonomy.
• Consultative Analytics: Provide detailed answers to key product questions and deliver recommendations backed by data to inform product team decisions.
• Data Enablement: Empower product teams by enhancing their data capabilities, enabling them to independently access, interpret, and analyze data.

Your Profile:

• Self-motivated and proactive, with a passion for data and a degree in economics, data science, mathematics/statistics, or a related field, coupled with several years of relevant experience in analytics, customer insights, data science, or data-driven consulting.
• Proven experience in product organizations, delivering insights and driving change for product teams and stakeholders. Ability to quickly grasp product and business contexts, understand requests, and uncover true needs.
• Expertise in SQL for querying large data sets, and proficiency in Python (DataFrames, Pandas, PyArrow, Jupyter Notebooks, RegEx, and plotting tools).
• Strong statistics skills, particularly for A/B testing and hypothesis validation, with a solid understanding of when and how to apply A/B testing.
• Proficient in building visualizations and reports using tools like Superset, PowerBI, MicroStrategy, QuickSight, Looker, or Tableau.
• Experience in data storytelling, able to present findings to senior stakeholders, including C-level executives, in a clear and compelling way.
• Strong communication, stakeholder management, and self-organization skills, acting as a key consultant to product managers and development teams.
• Hands-on experience with data engineering and data warehousing concepts, including building data pipelines using tools like Airflow, dbt, and version control systems like Git.
• Familiarity with data lakes and warehouses such as Redshift, Snowflake, BigQuery, and S3.
• Knowledge of Google Analytics or similar event-tracking tools and experience setting up tracking plans with product teams.
• Data modeling expertise, including building data abstraction layers.
• Ideally, some exposure to AWS and experience using Optimizely or similar experimentation tools.
• Fluency in English is required.