Data analyst

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Collectivus Holdings is a dynamic business group encompassing brands many of the most prestigious brands in the Collecting, Trading Card Game, and Hobby industries. With a century's worth of collective experience in driving product and service innovation, high-end quality standards, and elevated customer experience, Collectivus brands are among the most recognized and loved throughout the world by the communities they serve.

Website: https://www.collectivusholdings.com

Locations: Nashville – Garland – Aarhus – Lisbon – Worldwide

Job Title:

Data Analyst

Job Location:

Remote - US

We're looking for a Data Analyst to help Collectivus family of brands make faster, better-informed decisions. You'll partner with business leaders to understand their goals, turn raw data into clear reporting and analysis, and surface insights that drive growth and operational efficiency. This role blends technical work in SQL, Python, and BI tools with close collaboration across the business.

Responsibilities

Business Partnership & Insights

• Partner with vertical market leaders to understand business questions, define the problems worth solving, and translate them into clear analytical approaches.
• Analyze business performance to identify trends, opportunities, and risks, and present findings with specific, actionable recommendations.
• Prepare recurring and ad hoc management reports that summarize performance, highlight trends, and provide forward-looking forecasts.
• Communicate complex findings in plain language to both technical and non-technical audiences.

KPIs, Reporting & Dashboards

• Work with stakeholders to define, document, and standardize KPIs that measure progress on strategic goals across all Collectivus brands.
• Design, build, and maintain dashboards and self-service reports in BI tools such as Power BI, Tableau, or Looker.
• Monitor dashboard accuracy and usage, and continuously improve reporting based on stakeholder feedback.
• Automate recurring reports to reduce manual effort and improve turnaround time.

Data Extraction & Analysis

• Write efficient SQL queries to extract, join, and aggregate data from our Snowflake data warehouse.
• Use Python (e.g., pandas) to clean, transform, and analyze large datasets.
• Apply statistical methods such as trend analysis, segmentation, correlation, and hypothesis testing to answer business questions.
• Validate data quality and investigate anomalies before insights are shared.

Forecasting & Predictive Analytics

• Build and maintain forecasting and predictive models (e.g., sales forecasts, demand trends, customer behavior) to support planning and decision-making.
• Evaluate model performance over time and refine approaches as business needs change.

Data Governance & Process Improvement

• Help maintain data definitions, documentation, and reporting frameworks so metrics stay consistent across teams.
• Collaborate with data engineering to flag data gaps and improve pipelines and data models.
• Identify inefficiencies in business processes through data, and recommend improvements that increase efficiency and profitability.

Qualifications

• Bachelor's degree in Statistics, Computer Science, Finance, Economics, or a related quantitative field.
• 3–5 years of experience in data analysis, business intelligence, or a related analytics role.
• Strong SQL skills, including complex joins, window functions, and CTEs.
• Working proficiency in Python for data analysis (R is a plus).
• Hands-on experience with Snowflake or a similar cloud data warehouse (BigQuery, Redshift, Databricks).
• Experience building dashboards in Power BI, Tableau, or Looker.
• Solid understanding of statistical concepts and their practical business application.

Preferred

• Experience with forecasting or predictive modeling techniques.
• Familiarity with data modeling concepts and dbt or similar transformation tools.
• Experience in e-commerce, marketplace, collectibles, or multi-brand business environments.

Skills & Attributes

• Strong analytical and problem-solving skills, with the ability to structure ambiguous questions.
• High attention to detail and a commitment to data accuracy.
• Clear written and verbal communication, including data storytelling.
• Ability to manage multiple priorities and work independently in a fast-paced environment.

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