Data Analyst — AI-Driven Insights

About the Role 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. Key Responsibilities Core analysis & insight generation 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 AI-augmented workflows 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 Stakeholder communication 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 Data infrastructure & governance 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 Skills & Requirements Required 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 Preferred 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 Tech Stack Python SQL dbt Airflow Tableau Claude API OpenAI BigQuery Snowflake Looker Streamlit Plotly pandas scikit-learn Git What We Offer 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