Collectors is the leading creator of innovative technology that provides value-added services for collectors worldwide. We grade, authenticate, vault, and sell millions of record-setting collectibles, all while modernizing and digitalizing the process to further our mission of helping collectors pursue their passions. We’re always on the lookout for talented people to join our growing team. Our services span collectible trading cards, autographs, comic books, coins, video games, event tickets, and memorabilia. Our subsidiaries include PSA, PCGS, Beckett, SGC, and Card Ladder. Since our founding in 1986, we have graded and authenticated millions of items. We employ more than 3000 people across our headquarters in Santa Ana, California and offices in New Jersey, Texas, Florida, Japan, Shanghai, Hong Kong, Canada, Mexico, Germany, and France. We are hiring an Analytics Manager to join our Growth Analytics team. This team is responsible for the strategy and execution of scaling the core PSA grading business, marketplace business, and new initiatives. The ideal candidate is an independent thinker with a bias for action, and is equal parts analytical, strategic, and entrepreneurial. This candidate will be familiar with core concepts across the technology landscape and will have expertise in marketing analytics and/or product analytics. Intellectual curiosity is a must, and prior experience or interest in D2C businesses is a bonus. You’ll report directly to the Director of Growth Analytics. Remote or hybrid candidates will be considered. This will be discussed further as part of the recruiting process. What you’ll do: - Develop and own reporting and insights focused on customer activation and retention - Conduct in-depth analyses to identify opportunities to optimize existing customer experience flows, identify new opportunities, and inform business strategy; synthesize findings and make recommendations to stakeholders. - Partner with data platform, marketing and product teams to define key metrics necessary to meet business objectives - Build automated reports and dashboards to evaluate business performance and provide actionable insights to executives and stakeholders - Utilize the strategic insights you identify to lead and manage growth initiatives for the business Who you are: - 5+ years of experience in a relevant product, data, or analytics role, preferably with experience in consumer technology - Expert experience pulling large, complex datasets using SQL and building data abstractions to support reporting and analysis; experience building data pipelines with dbt a plus - Self-starter and natural problem solver who thrives in dynamic, fast-paced environments - Familiarity with data visualization tools like Tableau, Lightdash, Mode, Looker, etc. - Deeply analytical thinker who is passionate about using data to drive impact for customers and the business - Excellent written and verbal communication skills; highly organized and detail oriented - Bachelor’s degree is required, candidates holding a Master's degree or equivalent in a quantitative field is strongly preferred. Salary Range: The salary range for this position is $105,611 - $171,462. Actual compensation on this range varies based on a variety of non-discriminatory factors, including location, job level, experience, and skill set. This role may be eligible for bonuses, commissions, or other forms of compensation, please ask your recruiter for details. #BI-hybrid #BI-remote #LI-remote Reasons To Join Us: - Health Insurance: All full-time employees are eligible to enroll in Medical, Dental, and Vision - Additional Benefits: Full-time employees are eligible for fertility, commuter, and educational assistance benefits - 401(K) Matching Plan: We are proud to offer a competitive 401k matching plan to our employees to support their future financial goals - Vacation: All salaried employees are eligible for flexible time-off - Holiday Pay: All regular, full-time employees are eligible for ten company paid holidays - Employee Discounts: Employees receive discounts on select grading services for approved submissions - Flexible Hours: Many of our teams offer flexible schedules with varying shifts and will work with you to accommodate your needs - Fun Working Environment: Our team members are invited to participate in celebrations, holiday events, and team building activities Collectors uses e-Verify to validate your ability to work legally in the United States. We are aware that there are instances where individuals are receiving job offers that fraudulently allege to be from Collectors or one of our business units. This type of fraud can be carried out through false websites, through fake e-mails claiming to be from the company or through social media. We never ask for personal information such as your bank account, Social Security numbers or National IDs, nor do we send or request payments for the purchase of business-related equipment. If you suspect fraud, please reach out to jobs@collectors.com. We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, national origin, gender, sex, gender identity or expression, sexual orientation, age, citizenship, marital or parental status, disability, veteran status, or other class protected by applicable law. We believe that a team that represents a variety of backgrounds, perspectives, and skills will better service the diverse community of collectors we support. If you require an accommodation to apply or interview with us due to a disability or special need, please email people@collectors.com. U.S. residents: for disclosures relating to personal information we collect during the employment application and recruitment process, please see our Privacy Notice for U.S. Applicants. If you are based in California, you can read information for California residents here.

• Design, build, and maintain Philo's core dimensional models and data marts in dbt, serving as an owner of the analytics engineering layer of our data stack • Define and govern key business metrics and KPIs, ensuring consistent definitions and reliable calculations across all downstream reporting and analytics • Partner with stakeholders across the organization (Finance, Product, Marketing, Ad Sales, Content, and others) to understand their data needs and translate business questions into well-modeled, performant data products • Establish and enforce best practices for dbt project structure, model design patterns, documentation, and testing — raising the quality bar for analytics engineering at Philo • Build and maintain comprehensive data documentation and a data catalog so that analysts, data scientists, and business users can confidently self-serve • Implement and maintain data quality checks and monitoring (e.g., via dbt tests, BigEye, or similar tools) to proactively catch issues before they reach stakeholders • Optimize query performance and warehouse cost efficiency in Snowflake through thoughtful model design, materialization strategies, and incremental processing patterns • Collaborate with data engineers on pipeline design and data ingestion to ensure raw data lands in forms that support clean, efficient downstream modeling • Drive alignment across teams on data definitions, naming conventions, and a single source of truth — building stakeholder trust in data and reporting over time • Contribute to the evolution of Philo's overall data platform strategy, particularly as it relates to the semantic/metric layer, data governance, and analytics engineering tooling

Role Description We are currently seeking an AI Data Engineer - Security (Kafka Experience) to join our team in Remote, Karnātaka (IN-KA), India. - Data Engineer (With Kafka experience) - Kafka: Strong expertise in Kafka (4-5 years), with hands-on experience designing and operating large-scale, highly available event-streaming platforms, including: - Partitioning strategies - Consumer group optimization - Schema management - Performance tuning - API-first data ingestion: Strong hands-on pulling data from REST/GraphQL APIs with auth (OAuth2, API keys), pagination, rate limits, retries/backoff, and webhooks; strong Python skills to normalize/enrich data and land it cleanly into S3 (schema, partitioning, Parquet). - AWS data lake, end to end: Comfortable building/operating S3-based lakes with layered zones (raw → harmonized → conformed → modeled), Glue Data Catalog, IAM/Secrets Manager, VPC endpoints, encryption, lifecycle/versioning, and cost/perf best practices (file sizing, compaction). - AWS Glue + PySpark expert: Designs and optimizes Glue jobs using PySpark/DynamicFrames, bookmarks for incremental loads, dependency packaging, robust error handling, logging/metrics, and unit tests; knows how to tune jobs for scale and cost. - Airflow orchestration: Writes clean, parameterized, idempotent DAGs (sensors, SLAs, retries, alerts), manages dependencies across pipelines, and uses Git-based CI/CD to promote changes safely. - Snowflake proficiency: Builds ELT models (staging/ODS/marts), tunes performance (warehouse sizing, clustering, micro-partitions, caching), uses Streams/Tasks/Snowpipe for CDC, and follows solid RBAC and data governance practices. Qualifications - 4-5 years of experience with Kafka - Strong Python skills - Experience with AWS services - Proficiency in Snowflake - Experience with Airflow Requirements - Hands-on experience with REST/GraphQL APIs - Experience in building S3-based data lakes - Expertise in AWS Glue and PySpark - Ability to write clean and parameterized DAGs in Airflow - Knowledge of ELT models and data governance practices Benefits - Flexible work arrangements - Access to a robust ecosystem of innovation centers - Opportunities for professional growth and development

Job Title: Data Engineer Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for pipeline monitoring and data refresh cycles) About the Role: Our client is seeking a Data Engineer to design, build, and maintain reliable data pipelines and infrastructure that deliver clean, accessible, and actionable data. This role requires strong software engineering fundamentals, experience with modern data stacks, and an eye for quality and scalability. The Data Engineer ensures data flows seamlessly from source systems to warehouses and BI tools, powering decision-making across the business. Responsibilities: Pipeline Development: - Build and maintain ETL/ELT pipelines using Python, SQL, or Scala. - Orchestrate workflows with Airflow, Prefect, Dagster, or Luigi. - Ingest structured and unstructured data from APIs, SaaS platforms, relational databases, and streaming sources. Data Warehousing: - Manage data warehouses (Snowflake, BigQuery, Redshift). - Design schemas (star/snowflake) optimized for analytics. - Implement partitioning, clustering, and query performance tuning. Data Quality & Governance: - Implement validation checks, anomaly detection, and logging for data integrity. - Enforce naming conventions, lineage tracking, and documentation (dbt, Great Expectations). - Maintain compliance with GDPR, HIPAA, or industry-specific regulations. Streaming & Real-Time Data: - Develop and monitor streaming pipelines with Kafka, Kinesis, or Pub/Sub. - Ensure low-latency ingestion for time-sensitive use cases. Collaboration: - Partner with analysts and data scientists to provide curated, reliable datasets. - Support BI teams in building dashboards (Tableau, Looker, Power BI). - Document data models and pipelines for knowledge transfer. Infrastructure & DevOps: - Containerize data services with Docker and orchestrate in Kubernetes. - Automate deployments via CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI). - Manage cloud infrastructure using Terraform or CloudFormation. What Makes You a Perfect Fit: - Passion for clean, reliable, and scalable data. - Strong problem-solving skills with debugging mindset. - Balance of software engineering rigor and data intuition. - Collaborative communicator who thrives in cross-functional environments. Required Experience & Skills (Minimum): - 3+ years in data engineering or back-end development. - Strong Python and SQL skills. - Experience with at least one major data warehouse (Snowflake, Redshift, BigQuery). - Familiarity with pipeline orchestration tools (Airflow, Prefect). Ideal Experience & Skills: - Experience with dbt for transformations and data modeling. - Streaming data experience (Kafka, Kinesis, Pub/Sub). - Cloud-native data platforms (AWS Glue, GCP Dataflow, Azure Data Factory). - Background in regulated industries (healthcare, finance) with strict compliance. What Does a Typical Day Look Like? A Data Engineer’s day revolves around keeping pipelines running, improving reliability, and enabling teams with high-quality data. You will: - Check pipeline health in Airflow/Prefect and resolve any failed jobs. - Ingest new data sources, writing connectors for APIs or SaaS platforms. - Optimize SQL queries and warehouse performance to reduce costs and latency. - Collaborate with analysts/data scientists to deliver clean datasets for dashboards and models. - Implement validation checks to prevent downstream reporting issues. - Document and monitor pipelines so they’re reproducible, scalable, and audit-ready. In essence: you ensure the business has accurate, timely, and trustworthy data powering every decision. Key Metrics for Success (KPIs): - Pipeline uptime ≥ 99%. - Data freshness within agreed SLAs (hourly, daily, weekly). - Zero critical data quality errors reaching BI/analytics. - Cost-optimized queries and warehouse performance. - Positive feedback from data consumers (analysts, scientists, leadership). Interview Process: - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Task (e.g., build a small ETL pipeline or optimize a SQL query) - Client Interview with Engineering/Data Team - Offer & Background Verification