IT Intern

Summary: TUMI is a company known for creating world-class business and travel essentials. The IT Intern will work with various teams across the IT department to support Retail and E-commerce analytics by building pipelines, enhancing data quality, and deploying AI agents. Responsibilities: - Retail Data Ingestion & Pipelines by building or enhancing ingestion for common retail feeds. Implement Snowflake loading patterns with optional automation (if applicable) - Retail Data Modeling (SQL) by creating curated analytics-ready tables from raw feeds. Support omnichannel reporting by connecting various internal and external retail data elements - Data Quality & Reconciliation (with Agent Automation) by implementing retail-relevant checks like uniqueness/deduping of customers, referential integrity (orders, product/store IDs) and anomaly detection (drops/spikes in sales, cancellations, returns). Build an agent to automate quality check and reporting - Participate in system and integration testing in various other initiatives running in the departments - Build and Deploy Agents to Execute at Scale - from learning to Production - Phase 1 — Sandbox Learning (Weeks 1–3) Build a simple agent to generate validation SQL from templates and produce a report - Phase 2 — Controlled Pilot (Weeks 4–7) Expand to scheduled runs, consistent output, and alerting - Phase 3 — Scale & Hardening (Weeks 8–12) Add guardrails (scope-limited actions), logging/audit trail, performance controls, and human review workflows - Build ML Models for Forecasting & Predictions (Retail Use Cases) by working with a mentor to design and prototype a predictive model using curated datasets - Feature set definition (lags, rolling windows, promos, seasonality) - Testing approach (time-based splits) - Model training and evaluation - Forecast outputs written back into data tables for reporting - Basic explainability summary (top drivers / feature importance) - By the end of the internship, the person should have delivered: - Production-ready ingestion pipelines into Snowflake (raw → staging) - Curated retail data models (fact/dim tables) - A data quality suite with automated checks (SQL and Python) - At least one deployed agent that executes repeatable tasks at scale (logging and guardrails) - One forecasting/prediction prototype with documented accuracy - Documentation: data dictionary plus pipeline runbook plus lineage plus model card - A final demo showing measurable improvements (quality, timeliness, usability, or forecast accuracy) Required Qualifications: - Candidate must be enrolled/actively pursuing a degree in an IT-based field - High School diploma required - Working knowledge of SQL (joins, aggregations; window functions preferred) - Python basics (pandas, file/JSON handling, API calls) - Strong attention to detail; ability to learn fast - Clear communication and documentation mindset - Great organization skills, attention to detail, and time management skills - Excellent communication skills - Ability to work independently and as part of a team Preferred Qualifications: - Exposure of ecommerce concepts (orders, promotions, returns, inventory, POS) - Interest in retail/ecommerce data and data engineering - Exposure to Snowflake or any cloud data warehouse - Basic ML familiarity (time series, regression, evaluation metrics) - Familiarity with dbt, Airflow, or orchestration tools - Git/version control - Interest in AI/LLM concepts, evaluation, and safe automation patterns Required Skills: SQL, Python, Ecommerce concepts, Retail data engineering, Snowflake, Machine learning basics, dbt, Airflow, Git, AI/LLM concepts, Documentation mindset