Data Analyst — AI-Driven Insights

David's Bridal

2d•Hybrid

About The Position

We are looking for a Data Analyst who doesn't just answer questions — they can't stop asking them. You're the kind of person who pulls a report, spots something slightly off in row 47, and four hours later has uncovered a trend nobody knew existed. You're technically sharp enough to build what you need and curious enough to keep digging until the story is clear. This role sits at the intersection of rigorous analysis and modern AI. You will use LLMs, automation pipelines, and AI-assisted tooling to dramatically accelerate insight generation — but the engine behind it all is genuine intellectual curiosity and the drive to understand why, not just what. You will partner closely with product, marketing, operations, and leadership — not just to answer the questions on their roadmap, but to surface the ones they haven't thought to ask yet. Built for analysts who are technically fluent and genuinely curious The ideal candidate writes clean SQL and elegant Python — and also loses sleep over unexplained dips in conversion. You'll wield AI tools not because it's trendy, but because you want to spend more time on the interesting parts: the patterns, the hypotheses, and the so what.

Requirements

3–6 years in a data analyst or analytics engineer role — you've seen enough data to know when something doesn't add up
Advanced SQL — complex joins, window functions, CTEs; you write queries other analysts learn from
Strong Python — pandas, numpy, visualization; comfortable moving from notebook to production-ready script
Demonstrated use of AI/LLM tools to accelerate analysis, automate reporting, or build smarter workflows
A track record of finding insights that weren't in the original brief — proactive, not just reactive
Strong written communication — you can write a two-paragraph summary that makes a VP care about a cohort analysis
Statistical fundamentals — hypothesis testing, regression, cohort analysis, knowing when correlation isn't causation

Nice To Haves

Experience with dbt, Airflow, or similar data transformation/orchestration tools
Familiarity with cloud data warehouses — BigQuery, Snowflake, Redshift, or Databricks
Hands-on experience calling LLM APIs and building AI-assisted workflows
Exposure to machine learning — feature engineering, model evaluation, working with data scientists
Experience with RAG pipelines, embeddings, or vector search for analytics use cases

Responsibilities

Dig into data with genuine curiosity — follow threads, challenge assumptions, and don't stop at the surface-level answer
Design and execute investigations that surface non-obvious insights, not just metric summaries
Build and maintain SQL queries, Python scripts, and data models to support recurring and ad-hoc analysis
Own key performance dashboards and proactively flag when something looks interesting — or wrong
Use LLM APIs (e.g., Claude, GPT) to automate insight generation, narrative writing, and anomaly detection
Build natural language interfaces that allow non-technical stakeholders to query data without SQL
Design and maintain AI-powered reporting pipelines that deliver weekly/monthly commentary automatically
Develop and test prompts for structured data extraction, classification, and summarization tasks
Turn complex findings into clear, compelling narratives — you make people care about the data, not just read it
Come to meetings with a point of view, not just a chart; be willing to say what the data actually means
Partner with business teams to frame the right questions — and push back when the wrong questions are being asked
Collaborate with data engineering to define and document data models, transformations, and quality standards
Contribute to the team's data catalog and ensure analytical assets are documented and reproducible
Advocate for data quality, consistency, and governance across the organization

Benefits

Competitive salary commensurate with experience
Flexible hybrid work arrangement — 2–3 days in office per week
A culture that values curiosity — asking why is celebrated, not deflected
Access to cutting-edge AI tools and a team that actively encourages experimentation
Dedicated budget for courses, conferences, and certifications
Direct exposure to senior leadership and real ownership over insights that drive strategy
Opportunity to shape the AI analytics approach of a growing organization
Comprehensive benefits package
Rewarding Environment and Competitive Pay
Generous Dream Maker Discount After First Pay Period
Referral Incentive Program
Dayforce Wallet – Get Paid Early!
Health/Dental/Vision Insurance
401K Program
Paid Vacation, Wellness Days & Holidays, including your Birthday off!
Pet Benefits