Product Analytics Engineer

OdasevaSan Francisco, CA
355d$50,000 - $500,000

About The Position

Since 2012 Odaseva has helped global enterprises protect and secure their most valuable asset: data. Our platform and tools empower data-driven organizations to combat evolving threats, maintain operational integrity, and comply with data regulations. Our products include Backup and Restore, Archiving, Data Privacy solutions and much more. We’re a fast-growing scale-up with offices in San Francisco, Paris, Sydney, London, Kuala Lumpur, Singapore, and more. We serve a global customer base including Fortune 500 companies, government organizations, and NGOs, reaching more than 100 million Salesforce users worldwide. At Odaseva, our values — Trust, Service, Commitment, Excellence, Kaizen, and One Team — define the environment we foster for our employees to thrive and succeed. Due to significant growth and to address the needs of our Product, we are seeking a Data Solutions Engineer. This role is a unique blend of data engineering and data analysis, requiring both technical expertise and strong business acumen. You will be responsible for the development and maintenance of Odaseva's analytics and AI platform, ensuring data accuracy and scalability. You will also play a key role in delivering customer-facing analytics features and providing data-driven insights to guide product strategy and optimize investments. This role requires a strong understanding of data engineering principles, data analysis techniques, and product management methodologies.

Requirements

  • Bachelor’s degree in Data Science, Statistics, Computer Science, Economics, or a related field.
  • 7+ years of experience in a data analysis role, preferably within a product or tech environment.
  • Proven track record of owning and enhancing analytics platforms.
  • Advanced SQL skills for querying and optimizing large datasets.
  • Proficiency in data visualization tools (e.g., Salesforce CRM Analytics, Tableau, Looker).
  • Experience with statistical analysis and machine learning techniques.
  • Experience with data pipeline tools (e.g., Apache Airflow, dbt) for automating data workflows.
  • Familiarity with cloud-based data warehousing solutions (e.g., Snowflake, BigQuery, Redshift) for scalable and efficient data storage.
  • Knowledge of data modeling techniques (e.g., dimensional modeling, star schema) for designing effective data structures.
  • Experience with scripting languages for data processing (e.g., Python, R).
  • Understanding of data governance and security best practices.
  • Experience with statistical analysis methods for extracting insights from data.
  • Familiarity with machine learning techniques for predictive modeling and data mining.

Nice To Haves

  • Experience in delivering data products or reports to external clients.
  • Familiarity with product management frameworks and agile methodologies is a plus.
  • Experience with Salesforce and CRM Analytics is highly desirable.

Responsibilities

  • Own the development and maintenance of Odaseva's analytics & AI tools, ensuring data accuracy, reliability, and scalability.
  • Continuously enhance the platform to support evolving business needs and provide a robust foundation for data-driven insights.
  • Develop with the product team to build and deliver insightful product analytics features to our customers.
  • Become the authority on product usage, sales, and customer behavior data.
  • Deeply understand key performance indicators (KPIs), identify trends and patterns, and provide actionable insights to guide product strategy and roadmap planning.
  • Utilize data analysis to identify opportunities for enhancing product adoption and engagement.
  • Provide strategic guidance to prioritize investments and ensure resources are allocated effectively to drive maximum impact.
  • Translate complex data into clear and compelling narratives, using visualizations and presentations to communicate findings and recommendations.
  • Work closely with product management, engineering, marketing, and sales teams to gather requirements, understand business objectives, and ensure alignment on data-driven initiatives.
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