Pranit S.

I build the data platform underneath AI features. Warehouse, pipelines, retrieval and governance on Snowflake, BigQuery and Databricks, so your AI answers from numbers your finance team already agreed to. Most AI projects do not fail at the model. They fail underneath it. Retrieval returns the wrong record, nobody can tell whether an answer is grounded in a source, token spend is invisible until the invoice lands, and there is no way to know whether last week's prompt change made things better or worse. That layer is where I work, and it is the same layer as a warehouse done properly. Founder of Warehows Analytics. You work with me directly on every engagement. I have a team behind me when a build needs more hands, and I stay the person accountable for what reaches you either way. Official partner with Snowflake, dbt Labs, Databricks and Sigma Computing. Top Rated, 50+ projects delivered across SaaS, e-commerce, fintech, cybersecurity and private equity. $3M+ saved in client infrastructure costs. AI AND LLM WORK - Natural-language analytics over sensitive data: Snowflake Cortex Analyst for an energy private equity firm, letting the team query portfolio data conversationally without the data leaving its controls. - LLM-generated commentary on financial data: multi-tenant analytics for a finance SaaS serving 80+ customers, with the narration governed so it cannot state anything the underlying numbers do not support. - AI sentiment analysis at volume: 50,000+ customer reviews processed for a creative and PR agency, surfaced through Sigma dashboards. - RAG and retrieval pipelines: chunking and embedding strategy, hybrid vector and relational retrieval, and citation resolution so every statement traces to a source span instead of being asserted. - Cost and evaluation instrumentation: per-request token and spend attribution across providers, and evaluation sets so a model or prompt change is measured rather than hoped for. - Regulated and governed environments: least-privilege cloud IAM, data-residency pinning, and PII minimisation before anything reaches a model provider. THE PLATFORM UNDERNEATH IT - Fortune 500: Oracle to Snowflake migration, 10M+ records/day CDC pipelines. Query times down 85%, costs down 60%. - $50M e-commerce brand in Europe: Google Ads, Facebook, email and organic unified into a single attribution model. ROAS from 3:1 to 7:1. - Multi-brand e-commerce, four brands in Norway: Shopify, WooCommerce, QuickBooks and ad platforms consolidated into Databricks and Looker. +65% marketing ROI, 80% less reporting time. - Cybersecurity startup in the USA: BigQuery warehouse across Salesforce, RB2B and product data. dbt modelling resolved three conflicting definitions of "converted lead". - Veterinary clinic group in the USA: manual Excel replaced with live cross-clinic dashboards on Airbyte, Airflow, dbt, Snowflake and Sigma. 10x faster onboarding. - Marketing subscription platform in the USA: Snowflake reporting replacing a three-week manual Excel process. Runs daily, untouched. SERVICES · AI and LLM integration (RAG, conversational analytics, Cortex Analyst) · Cloud data warehouse design (Snowflake, BigQuery, Databricks) · ETL and ELT pipelines (Fivetran, Airbyte, Hevo, custom Python) · dbt transformation with testing, documentation and a semantic layer · BI dashboards (Sigma, Looker, Power BI, Superset, Streamlit) · Embedded analytics for SaaS with multi-tenant row-level security · Marketing attribution and revenue reporting · Legacy migration from Oracle, Talend and on-prem WHY THIS MATTERS Data and AI projects fail when nobody owns both ends. The pipeline engineer builds what was specified. The analyst reports what was delivered. The AI feature answers from whatever it was handed. Nobody checks whether any of it matches what the business needed. I hold the full picture, from raw source to the answer a director reads. Every model has tests. Every pipeline has monitoring. Every dashboard and every generated statement traces back to a definition settled before the first chart was built. I also tell you when I disagree. If the approach you have asked for will cost you money later, you hear it early, in writing, with the alternative attached. STACK · Snowflake, BigQuery, Databricks, PostgreSQL · dbt, Airflow, Fivetran, Airbyte, Hevo · Sigma, Looker, Power BI, Superset, Streamlit · Python, SQL, FastAPI · RAG and vector retrieval, Cortex Analyst, provider-agnostic LLM interfaces Send me the messy version of the problem rather than a tidied-up brief. I respond the same day. Top Rated · 50+ Projects · Snowflake Partner · dbt Partner · Databricks Partner · Sigma Computing Partner