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
Collectivus is building a modern data platform on Snowflake to serve its portfolio of brands. As a Data Engineer, you will design, build, and maintain the pipelines that move data from our source systems (ERP, CRM, e-commerce, marketing, survey, and finance platforms) into Snowflake, and shape it into reliable, well-modeled data for analytics, Power BI reporting, and AI-driven applications. You will work within an established architecture and set of engineering standards, partnering with the Data Architect on design decisions and with the Data Analysis team on delivery. Our data engineering team actively uses AI assistants in day-to-day development, and we expect engineers to adopt these tools thoughtfully and responsibly.
Responsibilities
Pipeline Development & Data Integration
- Build and maintain Python-based ETL/ELT pipelines that ingest data from REST APIs, SFTP/flat files, and SaaS platforms (e.g., Qualtrics, Shopify, UKG, ERP, HubSpot) into Snowflake.
- Implement incremental loads, MERGE-based upserts, watermark/control-table patterns, soft deletes, and idempotent reruns following team conventions.
Data Modeling & Warehousing
- Develop and tune Snowflake SQL across raw, staged, and presentation layers, including views, tasks, and streams.
- Design and build data marts in Snowflake (fact and dimension tables, conformed dimensions, and aggregate layers) that serve Power BI reporting and downstream analytics.
Orchestration, Security & Data Quality
- Schedule and orchestrate jobs using GitHub Actions, monitor runs, and resolve failures.
- Manage secrets and credentials securely via AWS Secrets Manager and Snowflake key-pair authentication.
- Build data quality checks and assertion tests, and detect and remediate schema drift from upstream sources.
AI & Advanced Data Services
- Support Snowflake Cortex and MCP-based data services (semantic views, search services) by preparing and governing the underlying data.
- Use AI coding assistants (e.g., Claude) as part of the daily workflow to accelerate pipeline development, code review, debugging, and documentation, while retaining full ownership of correctness and quality.
Collaboration, Documentation & Engineering Standards
- Collaborate with analysts and business stakeholders to translate reporting needs into data models and Power BI-ready datasets.
- Write clear runbooks and data dictionaries.
- Participate in code reviews and follow Git branching and pull request standards.
Qualifications
- 3–5 years of hands-on data engineering experience.
- Hands-on experience building star-schema data marts (fact/dimension design, grain definition, surrogate keys, and SCD handling) for BI consumption.
- Solid understanding of dimensional modeling and warehouse layering concepts.
- Strong Python (pandas, requests, boto3, snowflake-connector) for production pipelines.
- Advanced SQL and working experience with Snowflake or a comparable cloud data warehouse.
- Experience integrating with REST APIs, including authentication, pagination, and rate limiting.
- Familiarity with AWS services used in data workflows (S3, Lambda, Secrets Manager, IAM).
- Experience with CI/CD and job scheduling (Jenkins, GitHub Actions, or similar).
- Experience with Snowflake Cortex, semantic models, or LLM/AI-assisted data workflows.
- Demonstrated ability to work effectively with AI coding assistants, including strong prompting, critical review of generated code, and sound judgment on when not to rely on them.
Nice to Have
- Experience with industry-leading ETL tools, n8n, or similar low-code integration tools.
- dbt or an equivalent transformation and testing framework.
- Power BI data modeling or DAX exposure.
- Retail, wholesale, or consumer-products domain experience (ERP, CRM, PLM, e-commerce data).
Success in the First 6–12 Months
- Independently own eight to ten production pipelines end to end.
- Reduce recurring pipeline failures through improved monitoring and data quality checks.
- Contribute reusable pipeline templates that shorten onboarding time for new sources.
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