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Associate Data Product Manager (100% Remote, Anywhere in the USA)

Posted about 2 months agoViewed

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💎 Seniority level: Entry, 2+ years

📍 Location: USA

💸 Salary: 90000 - 120000 USD per year

🔍 Industry: Personal Finance

🏢 Company: GOBankingRates

🗣️ Languages: English

⏳ Experience: 2+ years

🪄 Skills: PythonSQLETLProduct ManagementProduct DevelopmentStrategyData engineeringCommunication SkillsDocumentation

Requirements:
  • Bachelor’s degree in Business, Data Science, Information Technology, or a related field.
  • 2+ years of experience in a data-related role or product management, preferably within a data-focused environment. Experience as a data analyst or data engineer is a plus.
  • Familiarity with data concepts, databases, and data visualization tools. Data profiling, attention to detail, and ad hoc analysis is a must-have.
  • Basic understanding of ETL processes and data modeling is a plus.
  • Ability to create SQL or Python scripts preferred.
  • Strong analytical and problem-solving skills with the ability to translate complex data needs into clear requirements.
  • Excellent verbal and written communication skills, with the ability to engage effectively with both technical and non-technical stakeholders.
  • Ability to work collaboratively in a fast-paced environment and manage multiple priorities.
Responsibilities:
  • Collaborate with business stakeholders to gather and understand their data needs and objectives, ensuring alignment with overall business strategy.
  • Help define clear and detailed requirements for data products, ensuring they meet the needs of both the business and the data engineering team.
  • Work closely with data engineers to prioritize and manage the development of data products, including data marts, schemas, ETL processes, dashboards, and reporting tools.
  • Create and maintain comprehensive documentation for data product specifications, processes, data mapping, and workflows to facilitate clear communication and understanding across teams.
  • Participate in UAT to validate data products against business requirements, ensuring high quality and usability.
  • Identify opportunities for enhancing data product offerings and processes and provide feedback to improve the overall data product lifecycle.
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