Product Analytics Engineer

Key Responsibilities:

• Analytics & AI Platform Ownership: 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. This includes aspects of data engineering like building and maintaining data pipelines, ensuring data quality, and optimizing data infrastructure.
• Customer-facing analytics: Develop with the product team to build and deliver insightful product analytics features to our customers, empowering them to make informed decisions based on their own data.
• Product metrics expertise: 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.
• Adoption & investment optimization: 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.
• Data-driven storytelling: Translate complex data into clear and compelling narratives, using visualizations and presentations to communicate findings and recommendations to both technical and non-technical audiences.
• Cross-functional collaboration: Work closely with product management, engineering, marketing, and sales teams to gather requirements, understand business objectives, and ensure alignment on data-driven initiatives.

Skills and Qualifications:

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Core Skills:
• 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.
Data Engineering Skills:
• Data Pipelines: Experience with data pipeline tools (e.g., Apache Airflow, dbt) for automating data workflows.
• Cloud Data Warehousing: Familiarity with cloud-based data warehousing solutions (e.g., Snowflake, BigQuery, Redshift) for scalable and efficient data storage.
• Data Modeling: Knowledge of data modeling techniques (e.g., dimensional modeling, star schema) for designing effective data structures.
• Scripting: Experience with scripting languages for data processing (e.g., Python, R).
• Data Governance: Understanding of data governance and security best practices.
• Statistical Analysis: Experience with statistical analysis methods for extracting insights from data.
• Machine Learning: Familiarity with machine learning techniques for predictive modeling and data mining.
Business Acumen & Communication:
• 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.
• Strong problem-solving skills and attention to detail.
• Excellent communication and presentation skills, with the ability to translate complex data into actionable insights.
Soft Skills:
• Self-motivated and proactive, with a passion for data and product development.
• Ability to work effectively in a cross-functional team environment.
• Excellent communication skills to convey technical concepts to non-technical stakeholders.