ApplyAssociate 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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