Data Manager and Analyst



IT, Data Science
Posted on Thursday, October 5, 2023

Position: Data Manager and Analyst

Company: Soundspace

Location: Flexible/Remote

About Soundspace:

Soundspace is an exciting platform in the thriving intersection of technology and music. Our mission is to empower the creative middle class. We are devoted to revolutionizing how creators, producers, and consumers engage with the music business, providing a framework for a sustainable and thriving industry. We are passionate about democratizing the creative industries of the world through affordable, reliable, and professional creative spaces.

Job Description:

Soundspace is on the lookout for a highly driven and analytical Data Manager and Analyst to steer our data-driven culture. This role is fundamental in extracting insights, crafting reports, and supporting our strategic objectives. Reporting to the Chief Operating Officer, the candidate will be at the helm of data management and analysis, playing an essential part in making data-informed decisions for Soundspace. This position is open to both fractional and full-time applicants.


  • Develop and maintain databases, data collection systems, data analytics, and other strategies that optimize statistical efficiency and quality.

  • Acquire data from primary or secondary data sources and maintain databases/data systems.

  • Design, create, and manage analytical frameworks to analyze large datasets.

  • Generate reports and data visualizations to present actionable insights to executive management and stakeholders.

  • Collaborate with cross-functional teams to define data requirements and provide insights for strategic decision-making.

  • Drive the creation of data-driven products and features to enhance Soundspace's offerings.

  • Manage data privacy and integrity, ensuring compliance with data protection regulations.

  • Continuously enhance data collection procedures to include information relevant for building analytic systems.

  • Assist the COO in evaluating organizational processes and strategies through data analytics.


  • A bachelor’s degree in Mathematics, Economics, Computer Science, Information Management, Statistics or a related field. Advanced degrees are a plus.

  • Proven experience in data management, data analysis, and reporting.

  • Strong knowledge of databases, data visualization tools, and statistical analysis.

  • Proficiency in SQL and experience with programming languages (e.g., Python, R).

  • Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.

  • Excellent communication and presentation skills.

  • Passion for the music industry and interest in its evolution.

  • Ability to work independently in a fast-paced environment.

What We Offer:

While this position does not offer a traditional salary, it provides equity compensation, making you an integral part of Soundspace's growth and success. You will have the rare opportunity to shape the future of a revolutionary player in the music industry through your insights and analytical acumen. Your work will be at the core of the strategic decision-making process, and your insights will fuel Soundspace's continuous innovation.

How to Apply:

Candidates are invited to submit their resume, a cover letter explaining their interest in the role, and any relevant work samples or portfolio pieces. Please submit your application through our online portal here and send an email to richard@soundspace.co ; we will reach out directly if we are interested in scheduling an interview.

Join us in harmonizing data and music at Soundspace!


Example KPI’s for the role:

  1. Data Accuracy: Achieve 98% accuracy in data collection and processing, with a monthly review and reconciliation process to validate data integrity.

  2. Report Timeliness: Ensure that 100% of scheduled reports are delivered on time to the relevant stakeholders, as per the agreed-upon schedule.

  3. Data Retrieval Speed: Improve data retrieval times by 20% over the next 6 months through optimization of database queries and systems.

  4. Data-driven Decisions: Increase the percentage of company decisions supported by data insights to 80% over the next quarter.

  5. Data Security Compliance: Ensure 100% compliance with data protection regulations and zero data breaches or violations over the next 12 months.

  6. Data Coverage: Expand data sources by adding at least two new relevant data sources each quarter to enrich the company's data ecosystem.

  7. Data Insight Utilization: Ensure that at least 75% of data insights generated are acted upon or used in decision-making processes each month.

  8. Data Quality Improvement: Reduce data quality issues (missing, inconsistent, outdated data) by 30% over the next 6 months.

  9. Stakeholder Satisfaction: Achieve a stakeholder satisfaction score of 85% or higher regarding data reports and insights on a bi-annual survey.

  10. Analytical Model Performance: Improve the performance of data analytical models by X% (based on relevant metrics such as accuracy, precision, recall) within the next 6 months.

  11. Data Training and Adoption: Conduct at least one data training session per quarter and achieve a 15% increase in data tool adoption rates across the company over the next year.

  12. Cost Savings through Data Insights: Identify and implement data insights that result in a 10% reduction in operational costs over the next fiscal year.


Average Day for a Data Manager and Analyst:

8:00 AM: Arrive at the office, grab a coffee, and check emails for any urgent data-related requests or issues that need immediate attention.

8:30 AM: Review the status of automated data collection processes, ensuring they ran successfully and check for any data integrity issues that need resolution.

9:00 AM: Attend the daily operations team meeting to discuss priorities for the day and update on the data insights generated.

10:00 AM: Dive into analyzing a dataset for an ongoing project, utilizing data analysis tools and scripting (such as SQL, Python, or R) to extract insights.

11:30 AM: Create a visualization report for the data analyzed to help stakeholders better understand the insights.

12:00 PM: Lunch break.

1:00 PM: Attend a meeting with the Chief Operating Officer to present data insights and discuss how they can be applied to make data-driven decisions.

2:00 PM: Work on optimizing database queries and improving data retrieval speed for regular reporting tasks.

3:00 PM: Monitor and manage data security measures, ensuring that data is stored and handled according to compliance regulations.

4:00 PM: Provide support and guidance to other departments on how to use data tools and interpret reports.

5:00 PM: Before wrapping up, set goals for the next day and ensure that any long-running data processing tasks are scheduled to run overnight.

5:30 PM: End of the day.

Average Week for a Data Manager and Analyst:


  • Set weekly objectives and KPIs.

  • Review automated data collection and processing tasks from the weekend.

  • Start analyzing data for a new project.


  • Continue deep-dive data analysis for ongoing projects.

  • Provide data and reports requested by various departments.

  • Optimize data retrieval and reporting processes.


  • Attend a mid-week operations meeting to present data insights.

  • Focus on data security and compliance measures.

  • Participate in a cross-departmental meeting to understand data needs.


  • Work on creating detailed data visualization reports.

  • Monitor ongoing data collection processes for any irregularities.

  • Assist other team members in data-related questions and tools.


  • Complete and distribute weekly data reports to stakeholders.

  • Have a meeting with the COO to discuss the impact of data insights on operations.

  • Review the week’s objectives and set new goals for the following week.

The role of a Data Manager and Analyst is dynamic and requires both deep analytical skills and the ability to communicate insights effectively. The tasks in this position may shift based on organizational priorities, data-related issues, and requests from other departments. Being adaptive and maintaining a keen eye for detail are crucial for success in this role.